SIMULATING PHYSICAL PROPERTIES OF REAL-WORLD OBJECTS

Apparatuses, systems, and techniques simulating physical properties of real-world objects. Data for a real-world object is collected. The collected data indicates one or more physical characteristics of the real-world object. A three-dimensional (3D) object is created based on some portion of the collected data. The 3D object has a format that is compatible with a 3D graphics platform. Physics simulation data associated with the 3D object moving within the 3D graphics platform is obtained. Image rendering data associated with the 3D object moving within the 3D graphics platform is stored with the computer system. The image rendering data is based at least in part on the obtained physics simulation data.

TECHNICAL FIELD

At least one embodiment pertains to systems and methods for simulating physical properties of real-world objects worn. For example, a user can request to access (e.g., using a client device) a simulated representation of a real-world object. A simulated representation of the real-world object can be generated based on data associated with the object, including image data, characteristic data, and physics simulation data, as well as user data associated with the user. The simulated representation can be provided for presentation on a graphical user interface (GUI) on a client device of the user.

BACKGROUND

Designers and developers of virtual environments (e.g., video game environments) utilize three-dimensional (3D) animation techniques to create immersive and visually captivating experiences for users accessing the virtual environments. Objects in these virtual environments can be significantly complex and detailed, in some instances. For example, a video game environment can include multiple different characters and/or other objects (e.g., scenery objects, game play objects, etc.) that each have its own characteristics and details, such as clothing items, accessories, gear, etc. It can be time consuming for a designer or developer to create and animate the behavior of each clothing item, accessory, gear, etc. for each character and/other object in the environment.

Virtual try-on (also referred to as “virtual fitting” or “digital try-on” technology) technology enables customers to explore or “try on” clothing items using a virtual avatar prior to purchasing the clothing items. The virtual try-on process involves generating a rendering of a clothing item that is worn by a virtual avatar associated with a user and providing the generated rendering to a client device associated with the user for presentation using a graphical user interface on the client device. Simulating clothing on a virtual avatar is a complex and challenging task that involves advanced computer graphics, computer vision, and, in some instances, machine learning techniques. It can be difficult for systems to provide a realistic and accurate representation of the physical characteristics of a clothing item on a virtual avatar.

DETAILED DESCRIPTION

Embodiments of the present disclosure relate to methods and systems for simulating physical properties of real-world objects. An online platform can provide, to users of the platform, information regarding with objects (e.g., real-world objects) associated with the platform. For example, a clothing retailer can sell clothing items to users using an e-commerce platform or an apparel services platform. The clothing retailer can provide, using the platform, information associated with real-world clothing items (e.g., a type, a size, a fabric, a color, etc.) that are available for purchase using the platform. A user of the platform can access the information provided by the clothing retailer (e.g., using a client device connected to the platform using a network, etc.) and can consider the provided information when determining whether to purchase a clothing item.

Information provided for an object using an online platform may not be sufficient to convey to a user certain characteristics associated with the object, which may otherwise be conveyed if the user interacts with the object in person. For example, even though size and/or fabric information associated with a real-world clothing item can be provided to a user using an e-commerce platform, the user may not get a sense of a fit of the clothing item (e.g., in view of the user's body type, etc.) and/or a behavior of the fabric of the clothing item based on this information. The user, however, may get a sense of the fit of the clothing item and/or the behavior of the fabric if the user interacts with the clothing item in person (e.g., if the user tries on the clothing item at home or at a brick-and-mortar store, etc.). In another example, a real-world clothing item can be a second-hand clothing item (e.g., a previously used and/or pre-owned clothing item) that includes one or more defects (e.g., through normal wear and tear, etc.). Although information provided using the e-commerce platform can indicate a defect associated with a real-world clothing item to a user, the user may not get a sense of where the defect exists on the clothing item and/or how the defect will look when the user is wearing the clothing item based on the provided information.

Some online platforms may provide a virtual fitting service that allows customers to virtually “try on” clothing items before making a purchase. The platform can generate or otherwise obtain a rendering of a virtual avatar associated with a user of the platform, where the virtual avatar is “wearing” or “trying on” a clothing item offered for sale using the platform. In conventional systems, a rendering of a clothing item is generated based on data provided by a manufacturer or designer of the clothing item. For example, a manufacturer or designer of the clothing item can provide, to the online platform, an indication of sizing information for the clothing item, a sewing pattern for the clothing item, a type of fabric that makes up the clothing item, a color or pattern of the clothing item, and so forth. Such data provided by the manufacturer or the designer represents target characteristics of the clothing item, which may not accurately reflect the actual characteristics of the clothing item after fabrication.

In some instances, a manufacturer or designer of a clothing item can provide image data depicting the clothing item to be used for generating the rendering of the clothing item. However, such image data is captured while the clothing item is worn by a mannequin or is laid flat on a surface, neither of which accurately reflect how the clothing item will look when worn by most, if not all, users of the platform. Further, such image data is enhanced (e.g., using photo or image enhancement software) to improve a lighting of the clothing item, a shape or structure of the clothing item, etc., as depicted by the image data, and/or to remove defects in the clothing item and/or staging items (e.g., tape, etc.) applied to the clothing item while the image data is generated. Accordingly, such image data that may be used to generate a rendering of a clothing item does not accurately reflect the actual characteristics (e.g., color, texture, fit, etc.) of the clothing item when worn by a user.

As indicated above, a rendering of a clothing item is generated based on target characteristic data and/or enhanced image data captured for the clothing item, in conventional systems. However, such data does not provide an indication of the actual, physical characteristics of the clothing item, such as a drape of the clothing item, a shape of the clothing item, a grain of the clothing item, a level of friction of the clothing item, a breathability of the clothing item, an elasticity of the clothing item, and so forth, when the clothing item is worn by a user of the platform. In addition, the enhanced image data used to generate the rendering does not provide an indication of how the clothing item will look to the user when the clothing item is in an environment with conditions that differ from the enhanced conditions of the environment including the clothing item when the image data was generated. Further, as indicated above, image data for the clothing item can be captured when the clothing item is worn by a mannequin, which is placed in a fixed position. Accordingly, the image data used to generate the rendering does not provide an indication of how the clothing item will look to the user when the virtual avatar is in a different position or is moving (e.g., walking, running, etc.) while wearing the clothing item.

For at least the reasons stated above, renderings of clothing items on virtual avatars generated by conventional systems do not give a user an idea or a sense of a fit of the real-world clothing object on the user, how one or more portions of the real-world clothing object will fall on the user, what the real-world clothing object will look like when the user is in different positions, states, and/or environments, and so forth. Data sets used to generate the renderings of clothing items can be significantly large and accordingly can take up a large amount of memory space of a computing system, which is therefore made unavailable to other data. Generating a rendering of a clothing item can further consume a significant amount of computing resources (e.g., processing cycles, etc.), which can make the computing resources unavailable to other processes. As described above, a rendering of a clothing item generated according to conventional techniques does not accurately represent the clothing item when worn by a user of the platform. Accordingly, the memory space and computing resources consumed for generating the rendering of the clothing item may be wasted in conventional systems. Further, a user that purchases a clothing item using an online platform that provides a rendering of a clothing item according to conventional techniques may end up returning the clothing item (e.g., as the rendering of the clothing item does not accurately reflect the physical characteristics of the real-world clothing item). Processes associated with purchasing and returning a clothing item can consume a large amount of computing resources (e.g., of a system associated with the online platform and/or a merchant of the clothing item and of a client device associated with the user). Such resources are unavailable for other processes, which can decrease the efficiency and increase the latency of the overall system.

In other or similar instances, a platform can provide users with access to tools to create and/or modify objects (e.g., three-dimensional (3D) objects) in a virtual environment. For example, a platform (e.g., a 3D graphics collaboration platform) can provide users (e.g., designers developers, etc.) with access to tools and/or resources for designing and/or developing 3D objects for inclusion in a virtual environment. Some objects can be complex and/or involve a high level of detail. For example, a character or object for inclusion in a video game environment can have several clothing items, accessories, gear, etc. that are unique to the character or object (e.g., distinct from clothing items, accessories, gear, etc. of other characters or objects). In some instances, each character and/or object in the virtual environment can have its own unique clothing items, accessories, gear, etc. that each have a high level of complexity or detail. Accordingly, it can take a designer or developer a significant amount of time (e.g., weeks, months, years, etc.) to create and animate each character or object, and the clothing items, accessories, gear, etc. associated with each character or object in a virtual environment. In some instances, 3D graphics design and development tools can consume a significant amount of computing resources (e.g., memory resources, processing resources, etc.) of a system. The larger the amount of time to create and animate a character or object (and the associated clothing items, accessories, gear, etc.), the larger amount of computing resources are consumed in the system. Such computing resources are unavailable to other processes of the system, which can increase an overall latency and decrease an overall efficiency of the system.

Further, platforms may generate or otherwise maintain model files for 3D objects designed, developed, or otherwise provided to the platform (e.g., by a designer or a developer). A model file refers to a collection of data and/or instructions that, when executed by a rendering engine, generates a rendering of a 3D object according to one or more animations. Some conventional 3D graphics design and development tools generate or otherwise update model files to be executed by rendering engines of advanced or otherwise complex processing units (e.g., graphics processing units (GPUs), etc.). In some instances, platforms can provide some users with access to 3D objects designed or otherwise developed by other users of the platform. Model files generated by the above described conventional 3D graphics design and development tools may not be executable using devices associated with such users (e.g., if the devices do not include or have access to advanced or otherwise complex processing units). Accordingly, such 3D objects may be inaccessible to such users.

Embodiments of the present disclosure address the above and other deficiencies by providing techniques for providing a simulated representation of a real-world object to users of a platform a. A platform (e.g., a 3D graphics collaboration platform, a software-as-a-service (SaaS) platform, etc.) can identify image data and/or characteristic data associated with a real-world object. In some embodiments, the real-world object can include a real-world clothing object, such as clothing items, jewelry items, and the like. It should be noted, however, that the real-world object can include any type of object, in accordance with embodiments of the present disclosure. The image data can include one or more images and/or a video depicting the real-world object. In some embodiments, the image data can include three-dimensional (3D) image data that is generated using a 3D scanning device. The characteristic data can indicate one or more physical characteristics of the real-world object. For example, the characteristic data can include a size of the clothing object, one or more colors of the clothing object, one or more measurements associated with the clothing object (e.g., sleeve length, etc.), one or more fabrics of the clothing object, and so forth. In some instances, the real-world clothing item can include one or more defects (e.g., a small hole in or under a sleeve, etc.). The characteristic data can include information associated with the one or more defects (e.g., a type of the defect, a location of the defect, a size of the defect, etc.), in some embodiments. The platform can receive the image data and/or the characteristic data from a client device associated with a retailer of the real-world clothing object, in some embodiments.

The platform can obtain physics simulation data associated with the real-world object based on at least the characteristic data associated with the real-world object. Physics simulation data can indicate or otherwise correspond to one or more physical states associated with the real-world object in view of a simulated physical behavior of the real-world clothing object. In some embodiments, the platform can obtain the physics simulation data from a physics simulation engine. The physics simulation engine can be configured to generate the physics simulation data based on the simulated physical behavior of the clothing object in view of soft-body dynamics associated with the clothing object. In an illustrative example, the real-world clothing object can be a silk dress. The physics simulation engine can generate, in view of at least the characteristic data associated with the silk dress, physics simulation data associated with a simulated behavior of the silk dress, such as a movement of the silk dress, a state of one or more portions of the silk dress when it is draped on another object, etc.

The platform can update one or more model files associated with generating a rendering of the real-world object to include information pertaining to one or more animations for the rendering of the real-world object based on the obtained physics data. In an illustrative example, the physics simulation data generated by the physics simulation engine can indicate a movement of a silk dress, a state of one or more portions of the silk dress when it is draped on another object, etc. The platform can update the one or more model files to include data and/or instructions for rendering an animation of a 3D representation of the silk dress according to the movement and/or draping simulated by the physics simulation engine.

In some embodiments, the platform can update rendering data associated with generating the rendering of the animation of the real-world object. In some embodiments, the rendering data can include data of one or more model files associated with generating the rendering of the animation of the real-world object. Each model file can correspond to a distinct rendering format for rendering the real-world object. A rendering format of a model file represents or otherwise defines data organization and compression techniques that are to be applied by a rendering engine to data and/or instructions of a model file to generate the rendering of the animation of a 3D object. In some instances, client devices and/or applications (or application instances) executing using the client devices can be configured to execute model files having particular rendering formats (e.g., according to a type of hardware components of the client devices, etc.). Each model file associated with generating the rendering of the animation of the real-world object that is updated by the platform can have a distinct rendering format. Accordingly, the model files updated by the platform can be executed by rendering engines associated with client devices and/or applications that support different types of model file rendering formats.

A client device of the user of the platform (or another platform) can transmit a request to access a simulated representation of the real-world object. Accordingly, the user is referred to herein as being interested in the real-world item (i.e., an “interested user”). In some embodiments, the platform can determine a rendering format associated with the client device and can identify a subset of the rendering data (e.g., a particular model file) associated with a rendering format that corresponds to the determined rendering format. In some embodiments, the platform can provide the identified subset of the rendering data to the client device for execution to generate the rendering of the real-world object according to the animations based on the obtained physics simulation data. In some embodiments, the client device can execute the model file (e.g., of the updated rendering data) to generate the rendering of the animation of the real-world object and can present the generated rendering to the user via a graphical user interface (GUI) of the client device, in some embodiments.

In some embodiments, the platform (or another platform) can generate the rendering of an avatar associated with the interested user. The avatar can be a realistic, life-like representation of the interested user, in some embodiments. In other or similar embodiments, features of the virtual avatar can be customized by the interested user (e.g., using virtual avatar customization tools of the virtual environment). The interested user can provide, using a client device associated with the interested user, image data and/or video data of the user and the platform can generate the rendering of the avatar based on the image data and/or video data. In additional or alternative embodiments, the platform can generate the rendering of the avatar based on biometric data (e.g., a height, a waist size, etc.) provided by the interested user (e.g., using the client device). The platform can generate the rendering prior to and/or responsive to receiving the request from the client device of the interested user.

The platform and/or the client device can generate a simulated representation of the real-world clothing object worn by the user based on the image data, the characteristic data, the obtained physics data, and user data (e.g., the biometric data, etc.) of the user. The platform can provide a rendering of the simulated representation of the real-world clothing object worn by the user represented by the avatar of the user for presentation of a graphical user interface on the client device of the user. The rendering of the simulated representation of the real-world clothing object with the rendering of the avatar can indicate to the user how the clothing object would fit the user, how one or more portions of the clothing object would fall on the user, and so forth, when the user wears the clothing object in the real world. In accordance with the previous illustrative example, the simulated representation of the silk dress indicates to the user how the silk dress would fall on the avatar based on the image data, the characteristic data, and/or the obtained physics simulation data. In another illustrative example, the real-world clothing object can include a defect, as described above. The rendering of the simulated representation of the real-world clothing object, including the defect, with the rendering of the avatar can indicate to the user how the defect will look on the user wearing the clothing object in the real world.

In some embodiments, the user can request, using the client device, to view the avatar in one or more particular positions or states (e.g., sitting, standing, walking, running, etc.) with the simulated representation of the real-world clothing object. The platform can update the rendering of the avatar based on the requested position or state. In some embodiments, the platform can update the simulated representation of the clothing object in view of the physics data obtained for the clothing object and the requested position or state of the avatar. The platform can provide the updated renderings of the avatar and the simulated representation of the clothing object for presentation using the GUI, in accordance with the request. In additional or alternative embodiments, the platform can update a rendering of an environment (e.g., lighting, weather, etc.) associated with the avatar and/or the simulated representation of the clothing object (e.g., in response to a request from the user). The updated rendering can indicate to the user how the real-world clothing object will appear and/or behave on the avatar in the respective environment.

Aspects and embodiments of the present disclosure provide techniques to provide users of a platform with access to a rendering of a real-world clothing object that accurately reflects the actual characteristics of the object, rather than target characteristics of the object. By using image data, characteristic data, and physics data to generate a simulated representation of a real-world clothing object worn by a user, embodiments of the present disclosure can provide to users an accurate representation of how a real-world clothing item will look and/or fit on the user, and the user can make a more informed decision on whether to purchase the real-world clothing item. As techniques of the present disclosure provides users with an accurate rendering of the real-world clothing items prior to purchasing, computing resources (e.g., processing cycles, etc.) consumed for generating the rendering of the clothing item is not wasted. Further, users that access (e.g., purchase) clothing items using an online platform that generates renderings according to techniques of the present disclosure are less likely to return clothing items, and therefore a fewer amount of computing resources of the system are consumed. Such resources are available for other processes of the system, which can increase an efficiency and decrease a latency of the overall system.

Aspects and embodiments of the present disclosure further provide techniques to enable users (e.g., designers, developers, etc.) of a platform to design or develop 3D objects in a virtual environment. Embodiments of the present disclosure enable designers and developers to create and/or customize 3D objects in a virtual environment based on image data captured for a real-world object (e.g., a real-world clothing object) and physics simulation data for the object obtained based on outputs of a physics simulation engine. Designers and developers therefore can create and/or customize the 3D objects in a smaller amount of time (e.g., hours, days, etc.), which can lead to a reduction in a number of computing resources (e.g., memory resources, processing cycles, etc.) consumed during the 3D object design and/or development process. Further, embodiments of the present disclosure enable platforms or systems to generate or otherwise maintain model files for 3D objects having different rendering formats that are supported by different types of client devices and/or applications (or application instances) executing using the client devices. Accordingly, a larger number of users can access 3D objects designed or developed using 3D graphics design and development tools. For example, model files can be generated or otherwise maintained having a rendering format associated with client devices that do not include advanced or otherwise complex processing units. The systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, these purposes may include systems or applications for online multiplayer gaming, machine control, machine locomotion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, deep learning, environment simulation, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, digital twin systems, cloud computing and/or any other suitable applications.

Disclosed embodiments may be comprised in a variety of different systems such as systems for participating on online gaming, automotive systems (e.g., a control system for an autonomous or semi-autonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented using a robot, aerial systems, medial systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems implemented using an edge device, systems incorporating one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems for generating or maintaining digital twin representations of physical objects, systems implemented at least partially using cloud computing resources, and/or other types of systems.

FIG.1is a block diagram of an example system architecture100, according to at least one embodiment. The system architecture100(also referred to as “system” herein) includes client devices102A-N, a data store110, a platform120(e.g., an e-commerce platform, a clothing services platform, etc.), a server machine160, and/or an artificial intelligence (AI) server180, each connected to a network104. In additional or alternative embodiments, system100can optionally include a platform140(e.g., a three-dimensional (3D) graphics collaboration platform) that is connected to client devices102A-N, data store110, platform120, server machine160, and/or AI server180using network104. In implementations, network104may include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a wired network (e.g., Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, and/or a combination thereof.

The client devices102A-N (collectively and individually referred to as client device(s)102herein) may each include computing devices such as personal computers (PCs), laptops, mobile phones, smart phones, tablet computers, netbook computers, network-connected televisions, etc. In other or similar embodiments, client devices102A-N can include or be connected to a virtual reality (VR) device (e.g., a VR headset) that is configured to provide a VR experience to a user of platform120and/or platform140. The VR device can be a monolithic VR device (e.g., a VR headset that includes a dedicated processor and/or power source) or another type of VR device, in some embodiments. In some implementations, client devices102A-N may also be referred to as “user devices.” Each client device may include a content viewer. In some implementations, a content viewer may be an application that provides a user interface (UI) for users to view or upload content, such as images, video items, web pages, documents, etc. For example, the content viewer may be a web browser that can access, retrieve, present, and/or navigate content (e.g., web pages such as Hyper Text Markup Language (HTML) pages, digital media items, etc.) served by a web server. The content viewer may render, display, and/or present the content to a user. The content viewer may also include an embedded media player (e.g., a Flash® player or an HTML5 player) that is embedded in a web page (e.g., a web page that may provide information about a product sold by an online merchant). In another example, the content viewer may be a standalone application (e.g., a mobile application or app) that allows users to view digital media items (e.g., digital video items, digital images, electronic books, etc.).

Platform120can provide users with access to renderings of one or more real-world objects. The real-world objects can be real-world clothing objects (sometimes referred to as clothing objects herein), in some embodiments. As noted herein, the real-world objects can include any type of real-world object, in some embodiments. “Clothing objects” is used herein for purposes of example and illustration only.

In some embodiments, platform120can be or correspond to a clothing services platform or other similar type platform. In other or similar embodiments, platform120can correspond to a 3D object design and development platform. As illustrated inFIG.1, platform120can include an object management engine122, a simulation engine124, and/or a model engine126, in some embodiments. In additional or alternative embodiments, object management engine122, simulation engine124, and/or model engine126can reside at one or more server machines (e.g., server machine160, another server machine not shown, etc.). In such embodiments, platform120can access object management engine122, simulation engine124, and/or model engine126using network104. Object management engine122can be configured to manage image data and/or characteristic data associated with a clothing object (e.g., provided by a user of a client device102). Simulation engine124can be configured to simulate one or more characteristics of the clothing object based on the image data and/or characteristic data for the clothing object. The simulation of the one or more characteristics of the clothing object can be represented by simulation data generated by simulation engine124. In some embodiments, simulation engine124can be a physics simulation engine. Model engine126can be configured to generate a model file that, when rendered, depicts a real-world clothing object. In some embodiments, model engine126can generate the model file based on image data, characteristic data, and/or simulation data associated with the real-world clothing object, as described herein. In other or similar embodiments, model engine126may obtain a model file (e.g., from object management engine122, from another engine or component of system100, etc.) and can update or otherwise modify the model file prior to rendering of the clothing object. Platform120(and/or platform140) can provide a rendering of the clothing object to a user associated with a client device102, in accordance with embodiments of the present disclosure. Further details regarding platform120, object management engine122and simulation engine124are provided with respect toFIG.2A.

Platform140can provide users with access to renderings of one or more virtual avatars (sometimes referred to as avatars herein), in some embodiments. In some embodiments, platform140can be or can otherwise correspond to a 3D graphics collaboration platform, such the Omniverse™ Platform by NVIDIA Corporation. A virtual avatar refers to a virtual character or representation associated with a user. The user can control the virtual avatar (e.g., using an associated client device102) and/or can interact with virtual avatars associated with other users using the application. In some embodiments, the virtual avatar associated with the user can be generated based on image data (e.g., photos, video data, etc.) provided to platform120(e.g., by the user) and can depict one or more characteristics of the user. In other or similar embodiments, the virtual avatar can depict one or more characteristics of a character selected by the user. It should be noted that embodiments of the present disclosure apply to any type of virtual avatar and/or any type of 3D object.

Platform140can include a user management engine142and/or a model engine144, in some embodiments. In additional or alternative embodiments, user management engine132and/or model engine144can reside at one or more server machines (e.g., server machine160, another server machine not shown, etc.). In such embodiments, platform140can access user management engine142and/or model engine144using network104. User management engine142can be configured to manage data associated with one or more users of platform140. In some embodiments, user management engine142can obtain data associated with a virtual avatar associated with the user (e.g., from client device102) and can store the obtained data at data store110. The obtained data can include image data associated with the user, one or more avatar characteristics associated with the virtual avatar (e.g., clothing style, hair style, hair color, accessories), and so forth. In some embodiments, the data associated with the virtual avatar can be obtained and/or updated in accordance with embodiments ofFIG.5. Model engine144can be configured to generate a model file that, when rendered, depicts a virtual avatar associated with a user. Model engine144can generate the model file based on the obtained user data, in some embodiments. In other or similar embodiments, model engine144may obtain a model file (e.g., from user management engine142, from another engine or component of system100, etc.) and can update or otherwise modify the model file prior to rendering of the virtual avatar.

Rendering engine162can render display data and/or image data from object data for transmission to and/or presentation by client device(s)102A-N. In some embodiments, rendering engine162can correspond to RTX Renderer™ from NVIDIA Corporation. Rendering engine162can leverage any number of processing units (e.g., graphical processing units (GPUs)) and/or nodes thereof for rendering the display data and/or image data from the object data. In some embodiments, rendering engine162can execute ray tracing (e.g., real time ray tracing) and/or path tracing using one or more GPUs to generate photo-realistic renderings of objects associated with object data. Object data can include data used by rendering engine162to render a 3D object (e.g., a real-world clothing object, a virtual avatar, etc.).

In some embodiments, the object data can indicate a bone structure associated with the 3D object, an indication of a mesh (e.g., a polygon mesh) for the 3D object, and/or an indication of one or more blend shapes (also referred to as morph targets) for the 3D object. The bone structure can include one or more bones that are each indicated by a bone index. The mesh can include one or more polygons made up of vertices, edges, and faces. Each blend shape can represent a distinct representation of at least a portion of the 3D object. For example, object data can include blend shapes for one or more faces of a virtual avatar, including a frowning face, a smiling face, and so forth. The object data can, in some embodiments, include an indication of a motion vector for each vertex of at least a portion of the mesh. A motion vector can indicate a degree and/or a direction of a motion of a respective vertex in accordance with an animation of the 3D object. In an illustrative example, a motion vector can include at least three values each indicating a degree of movement of the vertex according to a respective axis of motion (e.g., an x-axis, a y-axis, a z-axis, etc.). A positive value can indicate movement in a positive direction along the axis, while a negative value can indicate movement in a negative direction along the axis. In some embodiments, the object data can be included in a model file, such as a model file generated or otherwise obtained by model engine126and/or model engine144, as described above.

In some embodiments, rendering engine162can be associated with platform120. In such embodiments, platform120can generate first object data associated with a real world-clothing object based on the image data and/or the characteristic data associated with the real-world clothing object, as described above. In additional or alternative embodiments, the first object data can be generated based on simulation data obtained from simulation engine124, as described herein. Platform120can also obtain (e.g., from data store110, from platform140, etc.) second object data associated with a virtual avatar of a user of platform120and/or platform140. Platform120can provide the first object data and the second object data to rendering engine162and rendering engine162can generate a rendering of a real-world clothing object worn by a virtual avatar associated with a user based on the first object data and the second object data. Platform120and/or platform140can provide the rendering to a client device102for presentation to a user, in accordance with embodiments described herein. In other or similar embodiments, rendering engine162can reside at a client device102. In such embodiments, platform120and/or platform140can provide the first object data and the second object data to client device102(e.g., using network104). Rendering engine162at the client device102can generate the rendering of a real-world clothing object worn by a virtual avatar associated with a user based on the first object data and the second object data. The client device102can provide the rendering to a user using a UI of client device102, as described herein. Further details regarding platform120, platform140, and rendering engine162are provided herein.

As illustrated inFIG.1, system100can include an AI server180. In some embodiments, AI server522can include a generative model that can generate data in response to or otherwise associated with a request from a user of client device102. Further details regarding AI server180are provided with respect toFIG.6.

It should be noted that although some embodiments of this disclosure provide that platform120is a distinct platform from platform140, in additional or alternative embodiments, platform120and platform140can be or can otherwise correspond to the same platform. For example, components of platform120(e.g., object management engine122, simulation engine124, model engine126, etc.) can reside at or can be otherwise accessible to platform140. In another example, components of platform140(e.g., user management engine142, model engine144, etc.) can reside at or can be otherwise accessible to platform120. In other or similar embodiments, one or more components of platform120and/or platform140can reside at or can otherwise be accessible to other platforms not shown inFIG.1, in accordance with embodiments of the present disclosure.

It should be noted that althoughFIG.1illustrates object management engine122and simulation engine124as part of platform120, in additional or alternative embodiments, object management engine122, simulation engine124, and/or model engine126can reside on one or more server machines that are remote from platform120. It should also be noted that althoughFIG.1illustrates user management engine142and model engine144as part of platform140, in additional or alternative embodiments, user management engine142and/or model engine144can reside on one or more server machines that are remote from platform140. It should be noted that in some other implementations, the functions of platform120, platform14, server machine160and/or AI server180can be provided by more or a fewer number of machines. For example, in some implementations, components and/or modules of platform120, platform14, server machine160and/or AI server180may be integrated into a single machine, while in other implementations components and/or modules of any of platform120, platform14, server machine160and/or AI server180may be integrated into multiple machines. In addition, in some implementations, components and/or modules of server machine160and/or AI server180may be integrated into platform120and/or platform140.

In general, functions described in implementations as being performed platform120, platform14, server machine160and/or AI server180can also be performed on the client devices102A-N in other implementations. In addition, the functionality attributed to a particular component can be performed by different or multiple components operating together. Platform120and/or platform140can also be accessed as a service provided to other systems or devices through appropriate application programming interfaces, and thus is not limited to use in websites.

In implementations of the disclosure, a “user” can be represented as a single individual. However, other implementations of the disclosure encompass a “user” being an entity controlled by a set of users and/or an automated source. For example, a set of individual users federated as a community in a social network can be considered a “user.” Further to the descriptions above, a user may be provided with controls allowing the user to make an election as to both if and when systems, programs, or features described herein may enable collection of user information (e.g., information about a user's social network, social actions, or activities, profession, a user's preferences, or a user's current location), and if the user is sent content or communications from a server. In addition, certain data can be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user's identity can be treated so that no personally identifiable information can be determined for the user, or a user's geographic location can be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user can have control over what information is collected about the user, how that information is used, and what information is provided to the user.

FIG.2is a block diagram of an example platform120, object management engine122, simulation engine124, and model engine126, according to at least one embodiment. As described above, object management engine122, simulation engine124, and/or model engine126can reside at or can otherwise be connected to platform120(e.g., using network104). In other or similar embodiments, object management engine122, simulation engine124, and/or model engine126can reside at or can otherwise be connected to platform140(e.g., using network104). Object management engine122, simulation engine124, and/or model engine126can be connected to memory250, in some embodiments. Memory250can correspond to one or more portions of data store110, in some embodiments. In additional or alternative embodiments, memory250can correspond to any memory of, connected to, or accessible by a component of system100.

As described above, platform120can provide users with access to a rendering of a simulated representation of a real-world object. A real-world object can include or otherwise correspond to real-world clothing object, such as a clothing item, a jewelry item, an accessory item, or any other such item. As noted herein, the real-world object can correspond to any type of object, in accordance with embodiments of the present disclosure. In some embodiments, platform120can be or otherwise correspond to a clothing services platform. Users can access the rendering of the real-world clothing objects worn by an avatar associated with the user using a client device102connected to the clothing services platform, as described herein. In other or similar embodiments platform120can be or otherwise correspond to a 3D object design and development platform. Further details regarding such embodiments are described with respect toFIG.7below.

It should be noted that although some embodiments of the present disclosure may be described with respect to providing a rendering of a real-world clothing object with a virtual avatar and other embodiments of the present disclosure may be described with respect to providing a model file to a client device for execution to generate a rendering of a real-world object according to one or more animations, aspects of each embodiment can be applied to other embodiments of the present disclosure. For example, techniques associated with providing the rendering of the real-world clothing object can be applied to embodiments associated with providing the model file to the client device for execution to generate a rendering of a real-world object according to one or more animations, and vice versa.

FIG.3illustrates a flow diagram for an example method300for providing a rendering of a simulated representation of a real-world object worn by a user, according to at least one embodiment. In some embodiments, method300can be performed by platform120. For example, one or more operations of method300can be performed by object management engine122, simulation engine124, and/or model engine126, in some embodiments. Method300may be performed by one or more processing units (e.g., CPUs and/or GPUs), which may include (or communicate with) one or more memory devices. In at least one embodiment, method300may be performed by multiple processing threads (e.g., CPU threads and/or GPU threads), each thread executing one or more individual functions, routines, subroutines, or operations of the method. In at least one embodiment, processing threads implementing method300may be synchronized (e.g., using semaphores, critical sections, and/or other thread synchronization mechanisms). Alternatively, processing threads implementing method300may be executed asynchronously with respect to each other. Various operations of method300may be performed in a different order compared with the order shown inFIG.3. Some operations of the methods may be performed concurrently with other operations. In at least one embodiment, one or more operations shown inFIG.3may not always be performed.

At block310, processing logic identifies image data associated with a real-world clothing object and characteristic data associated with one or more characteristics of the real-world clothing object. In some embodiments, the image data and/or the characteristic data associated with the real-world clothing object can be provided by a user (e.g., a retailer user, distributor user, etc.) associated with platform120. Referring back toFIG.2, client device102A can be associated with a first user of platform120. The first user can be or can correspond to a retailer or distributor of clothing items, in some embodiments. Such user may be referred to herein as a “distributor user.” In some embodiments, client device102A can provide object data204associated with the real-world clothing object to platform120(e.g., in response to a request by the distributor user). The object data204can include the image data and/or the characteristic data, in some embodiments. In some embodiments, the image data can include one or more images (or image frames) each depicting one or more portions of the clothing object. The image data can be generated by a camera component of or connected to client device102A, in some embodiments. In some embodiments, the image data can include at least one of a set of 2D images depicting the clothing object, a video depicting the clothing object, and/or a set of 3D images depicting the clothing object. In one or more embodiments, the image data can be generated by a 3D scanner device. The 3D scanner device can be configured to capture 3D images of a subject. In such embodiments, the image data can include one or more 3D images depicting the clothing object. In an illustrative example, one or more images (or image frames) of the image data can depict a defect of the clothing object (e.g., a hole in the clothing object, etc.).

Characteristic data associated with a clothing object can be associated with one or more physical characteristics of the real-world clothing object. In some embodiments, the one or more physical characteristics of the clothing object can correspond to a size of the clothing object, a shape of the clothing object, a color of the clothing object, a design associated with the clothing object, one or more materials associated with the clothing object, and/or one or more defects associated with the clothing object. In some embodiments, the distributor user (or another user) associated with client device102A can provide at least a portion of the characteristic data associated with the clothing object to client device102A using one or more UI elements of a UI of client device102A. In such embodiments, client device102A can provide the characteristic data with the image data as object data204to platform120. In other or similar embodiments, the distributor user can provide an identifier associated with the clothing object, such as a stock keeping unit (SKU), a universal product code (UPC), etc. associated with the clothing object to client device102A using one or more UI elements of the UI of client device102A. The distributor user can additionally or alternatively provide an indication of a defect of the clothing object using the one or more UI elements, in some embodiments. Client device102A can provide identifier and/or the indication of the defect with the image data to platform120as object data204, in some embodiments.

As illustrated inFIG.2, object management engine122can include an image data component212and/or a characteristic data component214. Image data component212can extract image data from object data204provided to platform120by client device102A. Characteristic data component210can extract characteristic data from object data204. In some embodiments, image data component212and/or characteristic data component214can store the extracted image data at memory250as image data252and the extracted characteristic data at memory250as characteristic data254. As indicated above, in some embodiments, object data204can include image data252and may include an identifier associated with the clothing object, rather than characteristic data254associated with the clothing object. In such embodiments, characteristic data component214can obtain the characteristic data254associated with the clothing object from one or more other sources. For example, characteristic data component214can identify one or more clothing object databases that are accessible to platform120. Such databases can be publicly accessible databases and/or privately owned databases, in some embodiments. In some embodiments, the databases can include one or more entries that include a mapping between an identifier associated with a clothing object and an indication of one or more physical characteristics associated with the clothing object. Characteristic data component214can parse the entries of the database(s) to identify an entry corresponding to the identifier associated with the clothing object. Characteristic data component214can extract data indicating the one or more physical characteristics associated with the clothing object (e.g., mapped to the identifier) from the entry and can store such extracted data at memory250as characteristic data254.

At block312, processing logic obtains physics simulation data associated with the real-world clothing object based on at least the characteristic data. The physics simulation data corresponds to one or more physical states associated with the clothing object based on a simulated physical behavior of the clothing object. As described above, platform120can include or can be otherwise connected to simulation engine124. Simulation engine124can be, or can include a physics simulation engine that is configured to generate physics simulation data associated with a clothing object based on the simulated physical behavior of the clothing object in view of soft-body dynamics of the clothing object. The simulated physical behavior can correspond to a motion behavior associated with the clothing object and/or a draping behavior associated with the clothing object. In some embodiments, simulation engine124can be or can otherwise execute using PhysX® from NVIDIA Corporation, or other similar physical simulation techniques. In some embodiments, object management engine122can provide the characteristic data254and/or the image data252associated with the real world clothing object as input to simulation engine124. In other or similar embodiments, simulation engine124can obtain the characteristic data254and/or the image data252from memory250.

In some embodiments, simulation engine124can perform one or more simulations of a motion and/or a draping of the clothing object based on at least the characteristic data254associated with the clothing object. In some embodiments, simulation engine124can perform the simulation of the motion and/or the draping of the clothing object based on a simulated motion or activity of a potential user that is wearing the clothing object. The simulated behavior of the clothing object (e.g., the simulated motion and/or the simulated draping) can be represented as physics simulation data. Object management engine122and/or model engine126can obtain one or more outputs of simulation engine124. The obtained one or more outputs can include the physics simulation data (e.g., based on the outcome of the performed simulation(s)). The physics simulation data can be stored at memory250as simulation data256, in some embodiments.

Model engine126can generate and/or update a model file258associated with the clothing object based on the image data252, the characteristic data254, and/or the simulation data256. As indicated above, a model file258can include object data associated with a 3D object that, when rendered by a rendering engine (e.g., rendering engine162) depicts a representation of the 3D object. The object data can indicate a bone structure associated with the 3D object, a mesh for the 3D object, and/or an indication of one or more blend shapes for the 3D object. The object data can additionally or alternatively include a motion vector indicating a degree and/or a direction of a motion of a respective vertex in accordance with an animation of the 3D object. In some embodiments, model engine126can determine or otherwise obtain data associated with the bone structure, the mesh, and/or the blend shapes for the real-world clothing object based on the image data252and/or the characteristic data254associated with the clothing object. In additional or alternative embodiments, model engine126can determine or otherwise obtain data associated with the motion vector for the clothing object based on the characteristic data254and/or the simulation data256associated with the clothing object. Model engine126can include the obtained data described above in the model file258, in some embodiments. It should be noted that, in some embodiments, the clothing object can be represented as a mesh that is “worn” by a virtual avatar. In such embodiments, model engine126can determine or otherwise obtain data associated with the mesh for the clothing object based on the image data252and/or the characteristic data254of the clothing object, as described above. In some embodiments, model engine126can store model file258at memory250.

Referring back toFIG.3, at block314, processing logic identifies user data of a user interested in the real-world clothing object. In some embodiments, platform120can receive a request from a client device102(e.g., client device102B) associated with a second user of platform120to access a rendering of a real-world clothing object associated with a distributor user. In some embodiments, the request can include an indication of the second user (also referred to herein as the “requesting user”) and an indication of the clothing object. In an illustrative example, platform120can provide an application to client device102B that is accessible to the requesting user using a UI of client device102B. The application can provide an indication of each clothing object associated with one or more distributor users of platform120. In some embodiments, the application can be or correspond to a catalog of clothing objects hosted by platform120. The requesting user can engage with one or more UI elements of the UI associated with the application to provide a selection of a clothing object of interest to the requesting user. Client device102B can transmit the request to platform120(e.g., using network104) in response to the user selection.

As described above, platform140can be or otherwise correspond to a 3D graphics collaboration platform. In some embodiments, platform140can provide a user with access to a rendering of a virtual avatar associated with the user. Platform140can generate or otherwise obtain a model file that, when rendered (e.g., by rendering engine162) depicts the virtual avatar associated with the user. Further details regarding platform140and obtaining a model file for rendering a virtual avatar associated with a user are described with respect toFIGS.4and5below.

In some embodiments, platform140can provide platform120with access to a model file for rendering an avatar associated with the requesting user described above. In an illustrative example, platform120can provide an identifier of the requesting user (or client device102B) to platform140. Platform140can identify a model file associated with a virtual avatar for the requesting user and can provide the identified model file to platform120(e.g., using network104). As described above, platform120can be the same platform as platform140, in some embodiments. In such embodiments, platform120can access the model file (e.g., using memory250or another memory). In other or similar embodiments, platform120can provide model file258to client device102B and platform140can provide the model file associated with the virtual avatar of the requesting user (e.g., model file452described below) to client device102B (e.g., using network104).

Referring back toFIG.3, at block316, processing logic generates a simulated representation of the real-world clothing object, worn by the user, based on the image data, the characteristic data, the obtained physics simulation data, and the user data. As described above, model file258can be generated based on image data252, characteristic data254, and/or simulation data256. Platform120can provide model file258to rendering engine162(e.g., at server machine160and/or at client device102B) and rendering engine162can execute model file258to generate the rendering of the clothing object. In some embodiments, platform120and/or platform140can provide model file452(e.g., associated with the virtual avatar of the requesting user) to rendering engine162(e.g., with model file258). Rendering engine162can execute model file452(e.g., with model file258) to generate the rendering of the virtual avatar wearing the real-world clothing object. It should be noted that in some embodiments, model engine126and/or model engine144can generate a single model file that includes object data associated with the real-world clothing object worn by the virtual avatar, as described above. Such model file can be provided to rendering engine162and rendering engine162can generate the rendering of the virtual object wearing the real-world clothing object by executing such model file.

At block318, processing logic provides, for presentation on a user interface (UI) on a client device of the user, a rendering of the simulated representation of the real-world clothing object worn by the user represented by a virtual avatar associated with the user. In some embodiments, platform120and/or platform140can provide the rendering of the simulated representation of the clothing object worn by the user represented by the virtual avatar to client device102B. Client device102B can provide the rendering to the requesting user using a UI of client device102B, in accordance with previously described embodiments. As described above, in some embodiments, platform120and/or platform140can provide model file258and/or model file452to client device102B for rendering by rendering engine162at client device102B. Rendering engine162can execute the model files258,456, as described above, and client device102B can provide the generated rendering to the requesting user using the UI of client device102B.

In some embodiments, the UI of client device102B can include one or more UI elements (e.g., buttons, etc.) that can correspond to a movement of the clothing object and/or the virtual avatar. In some embodiments, the requesting user can engage with the UI elements to cause the clothing object and/or the virtual avatar to move. Client device102B can transmit an indication of the motion to rendering engine162and/or platform120, in some embodiments. Rendering engine162can update the rendering of the clothing object worn by the virtual avatar in accordance with the motion, as described above, and client device102B can provide the updated rendering to the requesting user using the UI. In some embodiments, one or more UI elements of the UI can correspond to an environmental condition of a virtual environment that includes the virtual avatar wearing the clothing object. The requesting user can engage with a UI element to change a condition (e.g., a lighting condition, a weather condition, etc.) of the virtual environment of the virtual avatar. Rendering engine162can update the rendering of the virtual avatar wearing the clothing object based on the changed condition associated with the UI element and client device102B can provide the updated rendering to the requesting user using the UI of client device102B, as described above. The renderings (and/or updated renderings) of the clothing object can provide the requesting user with an accurate representation of a behavior of the clothing object when worn by the user (e.g., in one or more environments). As described above, the clothing object can include one or more defects (e.g., holes, etc.). The rendering of the clothing object as worn by the virtual avatar can provide, to the requesting user, an accurate representation of the defect of the clothing object when worn by the user.

In one or more additional embodiments, platform120can determine a comfort level associated with the clothing object. The comfort level can indicate a level or degree of comfort of the clothing object in view of a size of the clothing object and dimensions of the virtual avatar of the user. In some embodiments, platform120can determine the comfort level based on the simulation data256and/or data associated with the requesting user (e.g., as described below). Platform120can provide an indication of the comfort level to the requesting user using the UI of client device102B, as described herein.

FIG.4is a block diagram of an example user management engine142and an example model engine144, according to at least one embodiment. As described above, user management engine142and/or model engine144can reside at or otherwise be connected to platform140(e.g., using network104). In other or similar embodiments, user management engine142and/or model engine144can reside at or otherwise be connected to platform120(e.g., using network104). User management engine142and/or model engine126can be connected to memory450, in some embodiments. Memory450can correspond to one or more portions of data store110, in some embodiments. In additional or alternative embodiments, memory450can correspond to any memory of, connected to, or accessible by a component of system100. In some embodiments, one or more portions of memory450can be included at memory250.

As described above, platform140can provide users with access to renderings of one or more virtual avatars. In some embodiments, platform140can be or can otherwise correspond to a 3D graphics collaboration platform. In some embodiments, a virtual avatar can be rendered to include one or more characteristics, as provided by the user of the platform. The one or more characteristics can be included with or otherwise indicated by user data402. User data402can include data associated with a user that is provided by or otherwise received by a client device102associated with the user. In some embodiments, the virtual avatar can be rendered to include one or more characteristics that are the same or similar to characteristics of the user (e.g., hair color, eye color, etc.). In such embodiments, the user data402can include an indication of one or more characteristics of the user, as provided using client device102. The indication of the one or more characteristics of the user can include image data for an image depicting the user, or other data that indicates the characteristics of the user, in some embodiments. In other or similar embodiments, the virtual avatar can be rendered as a character or object based on characteristics provided by the user. In such embodiments, user data402can include an indication of the characteristics of the character or object, as provided using client device102.

As illustrated inFIG.4, user management engine142can include a user data component422, a user avatar component424, and/or an activity data component426. User data component422of user management engine142can obtain user data402from platform120and, in some embodiments, can store the user data402at memory450. In some embodiments, user data component422can store a mapping between the obtained user data402and an identifier associated with the user and/or the client device102associated with the user at memory450.

In some embodiments, an artist or developer of a 3D object, such as the virtual avatar, can provide object data404associated with a virtual avatar to platform140(e.g., using a client device associated with the artist or developer). The object data404can include rendering data for default characteristics for the avatar, as defined by the artist or the developer. For example, the object data404can include an indication of a bone structure and a mesh (e.g., a polygon mesh) for the avatar, an indication of an assignment of one or more bones of the bone structure to a vertex of the mesh, and/or an indication of one or more default characteristics (e.g., indicated by default texturing coordinates, color data, etc.) for rendering the avatar. In some embodiments, platform140can store the object data404at memory450. User avatar component424of user management engine142can update object data404to include a mapping between one or more characteristics of the virtual avatar associated with the user of platform140to corresponding data of object data404. For example, user avatar component424can update object data404to include a mapping between one or more texturing coordinates and/or color data associated with a portion of the virtual avatar including the avatar's eyes and an eye color of the user, as indicated by user data402.

It should be noted that in other or similar embodiments, object data404and/or characteristics of a virtual avatar can be provided by another system other than platform140. For example, a computing system other than platform140can provide a model file452for rendering the virtual avatar using rendering engine162. The model file452can include object data404and/or characteristics of the virtual avatar, in some embodiments. In other or similar embodiments, a model engine144can generate or update the model file452based on the user data402and/or the object data404obtained from platform140. The model file452can include or otherwise correspond to a set of instructions that, when executed by a rendering engine (e.g., rendering engine162) can depict a virtual avatar having one or more characteristics, which as the characteristics indicated by user data402. In some embodiments, the model file452can include data or instructions relating to 3D modeling (e.g., creating a 3D mesh or wireframe model based on parameters defined by user data402, object data404, and/or other data), texturing (e.g., applying textures to the 3D model, including skin tone, hair color, eye color, and other surface details), rigging (e.g., creating a skeleton-like structure withing the 3D model, allowing the avatar to be animated and/or move realistically), and/or animation (e.g., relating to specific actions or gestures of the avatar), among other types of data or instructions.

In some embodiments, platform140can obtain model file452from model engine144and/or at memory450. Platform140can obtain model file452in response to a request, in some embodiments. For example, platform140can obtain model file452in response to a request from platform120. As described above, platform120can request model file452from platform140in response to a request from a user of platform120for a rendering of a real-world clothing object as worn by a virtual avatar of the user. In response to obtaining the model file452, platform120can provide the model file452to rendering engine162, in some embodiments. Rendering engine162can execute the model file452to generate the rendering of the virtual avatar. In some embodiments, platform120and/or rendering engine162can provide the rendering of the virtual avatar (e.g., rendered object456) to client device102, as described herein. In other or similar embodiments, platform140can provide an obtained model file452to platform120. Platform120can provide the model file452(e.g., with model file258) to rendering engine162(or another rendering engine) to obtain the rendering of the real-world clothing object as worn by a virtual avatar of the user, as described above.

In some embodiments, user management engine142and/or model engine144can update user data402and/or a model file452associated with a virtual avatar associated with a user of platform140based on activity data406obtained for the user. Activity data406can include any data that represents an activity (e.g., a physical activity, etc.) or motion performed by a user and detected by one or more devices460associated with the user. User management engine142can update user data402and/or object data404based on the activity data406, in some embodiments, and model engine144can update model file452based on the updated user data402and/or object data404, as described below with respect toFIG.5. The updated model file454can be stored at memory450and platform140can provide the updated model file454to platform120and/or rendering engine162, as described above.

FIG.5illustrates a flow diagram for an example method500for updating a virtual avatar associated with a user, according to at least one embodiment. In some embodiments, method500may be performed by platform140. In one or more embodiments, method500can be performed by user management engine142and/or model engine144. Method500may be performed by one or more processing units (e.g., CPUs and/or GPUs), which may include (or communicate with) one or more memory devices. In at least one embodiment, method500may be performed by multiple processing threads (e.g., CPU threads and/or GPU threads), each thread executing one or more individual functions, routines, subroutines, or operations of the method. In at least one embodiment, processing threads implementing method500may be synchronized (e.g., using semaphores, critical sections, and/or other thread synchronization mechanisms). Alternatively, processing threads implementing method500may be executed asynchronously with respect to each other. Various operations of method500may be performed in a different order compared with the order shown inFIG.5. Some operations of the methods may be performed concurrently with other operations. In at least one embodiment, one or more operations shown inFIG.5may not always be performed.

At block510, processing logic receives image data associated with a user of a platform and clothing size data associated with the user. The user can be a user of platform120, platform140, and/or another platform, in some embodiments. In some embodiments, the user can be associated with a virtual avatar of platform140(or another platform). Processing logic can receive the image data and/or the clothing size data from a client device102associated with the user. The image data and/or the clothing size data can be provided with user data402to platform140, in some embodiments. In other or similar embodiments, the image data and/or the clothing size data can be provided to platform140separately from user data402. In yet other or similar embodiments, the image data and/or the clothing size data can be received by platform140from platform120.

At block512, processing logic obtains activity data associated with one or more physical activities performed by the user. In some embodiments, platform140can obtain the activity data from one or more devices260associated with the user. A device260can include any type of device that is configured to monitor or otherwise obtain data indicating an activity or motion of a user. The device can include a wearable device or a non-wearable device, in some embodiments. In some embodiments, a device260can include, but is not limited to, a wrist-worn device (e.g., a smart watch), a fitness band device (e.g., worn on other areas of the body), a smart clothing device (e.g., a garment with one or more sensors and/or conductive fabric), a smart show device (e.g., shoes equipped with sensors and/or motion trackers), virtual reality (VR) fitness tracking device, and so forth. In some embodiments, device260can include or be connected to one or more types of sensors (e.g., accelerometers, gyroscopes, heart rate monitors, GPS modules, etc.) that can monitor a user's activity and can generate activity data (e.g., activity data406) indicating the monitored activity. Device260can provide the generated activity data406to platform140(e.g., using network104). In other or similar embodiments, a user can provide an indication of an activity or motion performed by the user to platform140(e.g., using a UI of client device102). For example, the user can engage with one or more UI elements of the UI of client device102to provide platform140with an indication of a running event that the user participated in.

Activity data406can include or otherwise indicate a step count associated with an activity or motion performed by a user, a distance traveled by a user during the performed activity or motion, a time period that the user performed the activity or motion, a number of calories burned during the performance of the activity or motion, a heart rate of the user during the performance of the activity or the motion, a sleep pattern associated with the user, a number of stairs climbed by the user and/or an elevation change of the user during a performed activity or motion, a time period during which the user remained inactive or sedentary, a stress level of the user during a period of activity or inactivity, and so forth. Activity data component426can obtain the activity data406and can store the activity data406at memory450, in some embodiments.

It should be noted that device260can provide platform140with activity data406at the request of the user associated with devices260and/or client device102. For example, during or after an initialization of devices206, the user can set one or more settings associated with the devices460indicating permission to provide activity data406to platform140. User can change the one or more settings (e.g., to revoke permission to provide activity data406) to platform140at any time. Immediately, or soon after, detecting that the user has changed the one or more settings, devices460can terminate transmission of activity data406to platform140. In some embodiments, a device460can transmit a notification to platform140indicating the revocation of the permission. In some embodiments, platform140can remove (e.g., erase, destroy, etc.) activity data406associated with the user from memory450and/or any other memory of system100.

At block514, processing logic updates an avatar associated with the user based on the clothing size data associated with the user and the obtained activity data. In some embodiments, processing logic can determine updated characteristics associated with a virtual avatar based on the activity data406and the existing characteristics of the virtual avatar (e.g., as indicated by user data402and/or object data404). The updated characteristics can include, in some embodiments, updated sizing and/or modeling characteristics for a 3D model of the virtual avatar. In some embodiments, the updated characteristics can be obtained based on one or more outputs of a machine learning model that is trained to predict, based on given data (e.g., user data402, object data404, image data, etc.) and given activity data of a past or current time period, a size or shape of a 3D model associated with a virtual avatar at one or more future time periods based on the activity data. The model can additionally or alternatively predict a future clothing size associated with one or more clothing objects based on the size or shape of the 3D model at the one or more future time periods. Platform140can provide user data402, object data404, activity data406, image data (e.g., depicting the user), and/or other types of data as input to the machine learning model. Platform140can obtain one or more outputs of the machine learning model, the one or more outputs indicating an updated size or shape of the 3D model based on the activity or motion of the given activity data406at one or more future time periods. For example, the one or more outputs can indicate a size or shape of the 3D model if the activity or motion of the given activity data406continues for one week, one month, multiple months, one year, etc. Platform140can extract the updated size or shape of the 3D model (e.g., at a particular future time period) from the one or more outputs of the machine learning model as characteristic data associated with the avatar, in some embodiments. In additional or alternative embodiments, the one or more outputs can indicate one or more clothing sizes associated with a real-world clothing item. Platform140can extract the one or more clothing sizes from the one from the one or more outputs, in some embodiments. In some embodiments, platform140can provide the extracted characteristics to activity data component426. In some embodiments, activity data component426and/or user avatar component424can update user data402and/or object data404based on the extracted characteristics, in some embodiments. It should be noted that the updated characteristics can be obtained according to other techniques, in other or similar embodiments.

At block516, processing logic provides a rendering of the updated avatar associated with the user for presentation using a UI of a client device (e.g., client device102) associated with the user. Model engine144can update model file452to obtain updated model file454based on the updated user data402and/or object data404, in some embodiments. Platform140can provide the updated model file454to platform120and/or rendering engine162to provide the rendering of the updated avatar for presentation using the UI of client device102, as described above. In some embodiments, platform140and/or platform120can provide an indication of the clothing size associated with a real-world clothing object worn by the updated avatar for presentation using the UI (e.g., with the rendering of the updated avatar).

It should be noted that platform140can update a rendering of a virtual avatar based on data other than activity data406. For example, a user can provide medical data to platform140indicating a pregnancy of the user. Platform140can update characteristics of a virtual avatar associated with the user based on stages of pregnancy at different future time periods, as described herein. The updated rendering of the avatar can be provided based on the updated characteristics, in some embodiments.

FIG.6is a block diagram of a system600including an AI server180, according to at least one embodiment. The system architecture600(also referred to as “system” herein) includes a data store610, a generative model620provided by AI server180, a server machine640with a query tool (QT)601, one or more client devices120, and/or other components connected to a network650. In some embodiments, network650may be a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a wired network (e.g., Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), and/or the like. In some embodiments, network650may include routers, hubs, switches, server computers, and/or a combination thereof.

In some embodiments, any of AI server180, server machine640, and/or client device(s)102may include a desktop computer, a laptop computer, a smartphone, a tablet computer, a server, a scanner, or any suitable computing device capable of performing the techniques described herein. In some embodiments, any of server machine540and/or client device(s)102may be (and/or include) one or more components or engines of system100ofFIG.1.

In some embodiments, data store610(database, data warehouse, etc.) may store any suitable raw and/or processed data. For example, data store610can store image data252, characteristic data254, simulation data256, user data402, object data404, activity data406, and so forth.

System600may further include a data manager (DM)660that may be any application configured to manage data transport to and from data store610, e.g., retrieval of data and/or storage of new data, indexing data, arranging data by user, time, type of activity to which the data is related, associating the data with keywords, and/or the like. DM660may collect data associated with one or more 3D objects for rendering using rendering engine162. DM560may collect, transform, aggregate, and archive such data in data store610. In some embodiments, DM660may support a suitable software that, with user's consent, resides on client device(s)102and/or devices460and tracks user activities. For example, the DM-supported software may capture user-generated content and convert the captured content into a format that can be used by various content destinations, e.g., QT601. In some embodiments, the DM-supported software may be a code snippet integrated into user's browsers/apps and/or websites visited by the user. Generating, tracking, and transmitting data may be facilitated by one or more libraries of DM660. In some embodiments, data may be transmitted using messages in the JSON format. A message may include a user digital identifier, a timestamp, name and version of a library that generated the message, page path, user agent, operating system, settings. A message may further include various user traits, which should be broadly understood as any contextual data associated with user's activities and/or preferences. DM660may track different ways the same user DM660may facilitate data suppression/deletion in accordance with various data protection and consumer protection regulations. DM660may validate data, convert data into a target format, identify and eliminate duplicate data, and/or the like. DM660may aggregate data, e.g., identify and combine data associated with a given user in the user's profile (user's persona), and storing the user's profile on a single memory partition.

Data store610may be implemented in a persistent storage capable of storing files as well as data structures to perform identification of data, in accordance with embodiments of the disclosure. Data store610may be hosted by one or more storage devices, such as main memory, magnetic or optical storage disks, tapes, or hard drives, network-attached storage (NAS), storage area network (SAN), and so forth. Although depicted as separate from the server machine640, data store610may be part of server machine640, and/or other devices. In some embodiments, data store610may be implemented on a network-attached file server, while in other embodiments data store610may be implemented on some other types of persistent storage, such as an object-oriented database, a relational database, and so forth, that may be hosted by a server machine640or one or more different machines coupled to server machine640using network650.

Server machine640may include QT601configured to perform automated identification and facilitate retrieval of relevant and timely contextual information for quick and accurate processing of user queries by generative model620, as disclosed herein. In some embodiments, QT601may be implemented by object management engine122. It can be noted that a user's request to access a rendering of a real-world clothing object worn by a virtual avatar associated with the user can be formed into a query that uses QT601in some embodiments. Using network650, QT601may be in communication with one or more client devices120, AI server180, and/or data store610, e.g., using DM660. Communications between QT601and AI server180may be facilitated by GM API602. Communications between QT601and data store610/DM660may be facilitated by DM API604. Additionally, GM API602may translate various queries generated by QT601into unstructured natural-language format and, conversely, translate responses received from generative model620into any suitable form (including any structured proprietary format as may be used by QT501). Similarly, DM API604may support instructions that may be used to communicate data requests to DM660and formats of data received from data store610using DM660.

A user (e.g., participant, etc.) may interact with QT601using a user interface (UI)642. In some embodiments, UI642may be the same or similar to a UI provided by platform120and/or platform140. In some embodiments, UI642may be implemented in a UI provided by platform120and/or platform140. For example, UI642can be a UI element of a UI provided by platform120and/or platform140. UI642may support any suitable types of user inputs, e.g., speech inputs (captured by a microphone), text inputs (entered using a keyboard, touchscreen, or any pointing device), camera (e.g., for recognition of sign language), and/or the like, or any combination thereof. UI642may further support any suitable types of outputs, e.g., speech outputs (using one or more speaker), text, graphics, and/or sign language outputs (e.g., displayed using any suitable screen), file for a word editing application, and/or the like, or any combination thereof. In some embodiments, UI642may be a web-based UI (e.g., a web browser-supported interface), a mobile application-supported UI, or any combination thereof. UI642may include selectable items. In some embodiments, UI642may allow a user to select from multiple (e.g., specialized in particular knowledge areas) generative models620. UI642may allow the user to provide consent for QT601and/or generative model620to access user data previously stored in data store610(and/or any other memory device), process and/or store new data received from the user, and the like. UI642may allow the user to withhold consent to provide access to user data to QT601and/or generative model620. In some embodiments, user inputs entered using UI642may be communicated to QT601using a user API644. In some embodiments, UI642and user API644may be located on client device102that the user is using to QT601. For example, an API package with user API644and/or user interface642may be downloaded to client device102. The downloaded API package may be used to install user API644and/or user interface642to enable the user to have two-way communication with QT601.

QT601may include a user query analyzer603to support various operations of this disclosure. For example, user query analyzer603may receive a user input, e.g., user query, and generate one or more intermediate queries to generative model620to determine what type of user data GM620might need to successfully respond to user input. Upon receiving a response from GM620, user query analyzer603may analyze the response, form a request for relevant contextual data for DM660, which may then supply such data. User query analyzer603may then generate a final query to GM620that includes the original user query and the contextual data received from DM660. In some embodiments, user query analyzer603may itself include a lightweight generative model that may process the intermediate query(ies) and determine what type of contextual data may have to be provided to GM620together with the original user query to ensure a meaningful response from GM620.

QT601may include (or may have access to) instructions stored on one or more tangible, machine-readable storage media of server machine640and executable by one or more processing devices of server machine640. In one embodiment, QT601may be implemented on a single machine (e.g., as depicted inFIG.5). In some embodiments, QT601may be a combination of a client component and a server component. In some embodiments QT601may be executed entirely on the client device(s)102. Alternatively, some portion of QT601may be executed on a client computing device while another portion of QT601may be executed on server machine640.

FIG.7illustrates a flow diagram for an example method of providing a rendering of a real-world object, according to at least one embodiment. In some embodiments, method700can be performed by platform120. For example, one or more operations of method700can be performed by object management engine122, simulation engine124, and/or model engine126, in some embodiments. Method700may be performed by one or more processing units (e.g., CPUs and/or GPUs), which may include (or communicate with) one or more memory devices. In at least one embodiment, method700may be performed by multiple processing threads (e.g., CPU threads and/or GPU threads), each thread executing one or more individual functions, routines, subroutines, or operations of the method. In at least one embodiment, processing threads implementing method700may be synchronized (e.g., using semaphores, critical sections, and/or other thread synchronization mechanisms). Alternatively, processing threads implementing method700may be executed asynchronously with respect to each other. Various operations of method700may be performed in a different order compared with the order shown inFIG.7. Some operations of the methods may be performed concurrently with other operations. In at least one embodiment, one or more operations shown inFIG.7may not always be performed.

At block710, processing logic collects data for a real-world object (e.g., a real-world clothing object, another type of real-world object, etc.). The data can indicate one or more physical characteristics of the real-world object. In some embodiments, the data can include image data and/or characteristic data associated with the real-world object, as described above. The real-world object can be provided by a user (e.g., a designer, a developer, etc.) associated with platform120, in accordance with previously described embodiments. The one or more physical characteristics of the real-world object can correspond to at least one of a size of the real-world object, a shape of the real-world object, a design associated with the real-world object, one or more materials associated with the real-world object, or one or more defects associated with the real-world object, as described herein.

In some embodiments, the identified data can be image data that includes a set of 2D images, a video, a set of 3D images, etc. depicting the real-world object. Processing logic can provide the image data as input to an artificial intelligence (AI) model that is trained to predict, based on given image data, one or more physical characteristics associated with objects depicted in the given image. The AI model can be trained based on a data set including historical image data and historical characteristic data for one or more objects depicted in an image of the historical image data, in some embodiments. In one example, the data set can include a subset of training inputs and a subset of target outputs. The subset of training inputs can include the historical image data. The subset of training inputs can indicate, for one or more objects depicted in an image of the historical image data, one or more physical characteristics associated with the one or more objects. Processing logic can obtain one or more outputs of the AI model and can extract, from the obtained one or more outputs, characteristic data indicating the one or more physical characteristics of the real-world object depicted in an image of the given image data. In some embodiments, the one or more outputs can include one or more sets of characteristic data and, for each set of characteristic data, an indication of a level of confidence that the respective set of characteristic data corresponds to the real-world object depicted in an image of the given image data. Processing logic can extract the characteristic data from the one or more outputs by identifying the set of characteristic data having a level of confidence that satisfies one or more confidence criteria (e.g., exceeds a threshold level of confidence, is larger than other levels of confidence for other sets of characteristic data, etc.). In some embodiments, processing logic can use the extracted characteristic data to obtain physics simulation data associated with the real-world object, in accordance with embodiments described herein.

At block712, processing logic can create a 3D object based on some portion of the collected data. In some embodiments, processing logic can create the 3D object based an output of a NeRF engine, as described above. For example, processing logic can provide the collected data as input to a NeRF engine that generates data (e.g., of a model file) for the 3D object based on the given input data. As indicated above, the NeRF engine can include, correspond to, or implement techniques of NGP Instant NeRF™ by NVIDIA Corporation.

At block714, processing logic obtains physics simulation data associated with the 3D object moving within a 3D graphics platform. The 3D graphics platform can be a 3D graphics collaboration platform, such as the Omniverse™ Platform by NVIDIA Corporation. The physics simulation data can correspond to one or more physical states associated with the real-world object based on a simulated physical behavior of the real-world object, as described above. In some embodiments, processing logic can obtain the physics simulation data by providing the data associated with the real-world object as input to a physics simulation engine. The physics simulation engine is configured to generate physics simulation data associated with the real-world object based on the simulated physical behavior of the real-world object in view of soft-body dynamics and/or rigid-body dynamics associated with the real-world object. In some embodiments, processing logic can provide the characteristic data extracted from the output(s) of the AI model as input to the physics simulation engine, as described above. In some embodiments, the physics simulation engine can be or can otherwise execute using PhysX® from NVIDIA Corporation.

In some embodiments, a user of platform120can provide an indication (e.g., via client device102) of one or more simulations that are to be simulated by physics simulation engine. In an illustrative example, a designer or developer can be creating or otherwise customizing an animation of a clothing object on a character or object in a virtual environment (e.g., a video game environment, etc.). The real-world clothing object can serve as a base for the animated clothing object for the character or the object. The designer or developer can provide, via client device102, an indication of one or more movements of the character or the object (e.g., according to abilities of the character or object in the virtual environment) and client device102can provide the indication to platform120(e.g., via network104). The physics simulation engine can generate physics simulation data based on a simulation of the indicated movements.

At block716, processing logic generates and/or updates image rendering data associated with generating a rendering of the real-world object based on the obtained physics simulation data. The updated rendering data can include one or more model files associated with generating the rendering of the real-world object. The one or more files include information pertaining to one or more animations for the rendering of the real-world object based on the obtained physics simulation data. In some embodiments, processing logic can generate and/or update the model file based on the data associated with the real-world object and/or the physics simulation data obtained for the real-world object. As described above, in some embodiments, image data associated with the real-world object can be provided to a neural radiance field (NeRF) engine that is configured to generate a model file to render the real-world object as a 3D object. The NeRF engine can generate the model file for the 3D object based on 2D images, in some embodiments. In some embodiments, the NeRF engine can include, correspond to, or implement techniques of Neural Graphics Primitives (NGP) Instant NeRF™ by NVIDIA Corporation. In some embodiments, the model files can be additionally or further updated to include instructions associated with an animation of the real-world object according to the simulations performed by the physics simulation engine, in accordance with embodiments described herein.

In some embodiments, each model file associated with generating a rendering the real-world object can have a distinct rendering format. As provided above, a rendering format of a model file represents or otherwise defines data organization and compression techniques that are to be applied by a rendering engine to data and/or instructions of a model file to generate the rendering of the animation of a 3D object. In some instances, client devices and/or applications (or application instances) executing using the client devices can be configured to execute model files having particular rendering formats (e.g., according to a type of hardware components of the client devices, etc.). Each model file associated with generating the rendering of the animation of the real-world object that is updated by the platform can have a distinct rendering format. Accordingly, the model files updated by the platform can be executed by rendering engines associated with client devices and/or applications that support different types of model file rendering formats. Examples of model file rendering formats include, but are not limited to a graphics library transmission format binary file format (e.g., GLB format), a Filmbox (FBX) format, a geometry definition file format (e.g., OBJ format), a universal scene description-based format (e.g., USD format, USDZ format, etc.), a standard tessellation language (STL), a standard for the exchange of product data (STEP) format, a collaborative design activity (COLLADA) format, and any other such formats for model files.

At block718, processing logic stores the image rendering data with a computing system. In some embodiments, processing logic can store the model files at a memory associated with platform120. For example, processing logic can store the model files as model files258A-N at memory250and/or as model files452A-N at memory450. In some embodiments, processing logic can store an indication of the rendering format associated with each model file at memory250and/or memory450.

It should be noted that embodiments the present disclosure provide that rendering data can include one or more model files for generating the rendering of the real-world object. However, rendering data can include any type of data used to generate the rendering of the real-world object. For example, the rendering data can include a data of a data structure (e.g., residing at memory250and/or memory450) that is used to generate the rendering of the real-world object. In another example, the rendering data can include data that is referenced by a model file to generate the rendering of the real-world object.

At block720, processing logic receives a request from a client device for access to the image rendering data associated with the rendering of the 3D object. At block722, processing logic determines a rendering format associated with the client device. In some embodiments, an indication of the rendering format can be included with the request for access to the one or more model files. In other or similar embodiments, the request can include an indication of a type of the client device, a type of one or more hardware components of the client device, an application (or application instance) executing using the client device, and so forth. Processing logic can determine the rendering format that corresponds to the client device based on the indication of the request. For example, processing logic can access a data structure (e.g., a table) that includes entries that map a rendering format to a type of client device, a type of hardware components, a type of an application, etc. The data structure can be stored at memory250and/or at memory450, in some embodiments. Processing logic can identify an entry of the data structure that corresponds to the type of the client device, the type of one or more hardware components of the client device, the application (or application instance) executing using the client device, etc. to determine the rendering format associated with the client device.

At block724, processing logic identifies a subset of the image rendering data (e.g., a model file, etc.) having a distinct rendering format that corresponds to the determined rendering format associated with the client device. As indicated above, processing logic can generate and/or update model files (e.g., according to block714) that each have a distinct rendering format. Processing logic can identify the model file of the generated and/or updated model files that has the rendering format corresponding to the rendering format determined for the client device. In some embodiments, processing logic may determine that the model files generated and/or updated for the rendering of the animation of the real-world object do not have a rendering format that corresponds to the determined rendering format for the client device. In such embodiments, processing logic can generate and/or update a model file to have the determined rendering format for the client device, in some instances, can store the generated and/or updated rendering file with model files258A-N at memory250and/or with model files452A-N at memory450, as described above.

At block726, processing logic provides identified subset of the image rendering data (e.g., the identified model file) to the client device for execution to generate the rendering of the real-world object according to the one or more animations. A rendering engine at or otherwise accessible to the client device can execute the model file according to the rendering format associated with the client device, as described herein. Upon execution of the model file, a rendering of the animation of the real-world object can be generated. The client device can provide a user with access to the rendering of the animations via a GUI of the client device, as described herein.

In additional or alternative embodiments, processing logic can receive an additional request from an additional client device for access to the one or more model files associated with the rendering of the real-world object. In response to receiving the additional request, processing logic can determine a rendering format associated with the additional client device. The determined rendering format associated with the additional client device can be distinct from the rendering format determined for the client device (e.g., described with respect to blocks716-722ofFIG.7). Processing logic can identify a model file associated with the rendering format determined from the additional client device and can provide the identified model file to the additional client device, as described above. As illustrated herein, embodiments of the present disclosure enable the platform to generate model files that can be executed by rendering engines according to distinct rendering formats (e.g., that are supported by client devices accessing the model files).

As described herein, embodiments of the present disclosure enable a designer and/or developer of a 3D object to create or customize an animation of a real-world object. In some embodiments, the designer and/or the developer may wish to make changes to the rendering of the real-world object (e.g., to include customized features or details). In some embodiments, the designer and/or the developer can update the rendering of the real-world object using 3D object animation tools, such as those provided by GeForce™ by NVIDIA Corporation. The designer and/or the developer can generate or otherwise obtain the 3D object depicting the real-world object using other techniques of image capture for animation (e.g., by NVIDIA Corporation). In other or similar embodiments, platform120can provide one or more tools or interfaces that enable the rendering of the real-world object to be updated based on one or more outputs of generative model620620, described with respect toFIG.6. In an illustrative example, a designer and/or a developer (or other such type of user of platform120) can interact with QT601using a UI642to provide a user query. The user query can indicate one or more modifications or changes to the rendering of the real-world object. In some embodiments, the user query can be provided in a natural language to the designer and/or the developer. The query can be provided to GM620, in accordance with above described embodiments. In some embodiments, the one or more model files associated with the rendering of the real-world object can be provided to GM620. Platform120can obtain one or more outputs from GM620and can extract, from the one or more outputs, data reflecting the update to the rendering of the real-world object, as requested by the designer and/or developer. The update data extracted from the one or more outputs can include updated data and/or instructions associated with rendering the animation of the real-world object. Platform120can update the model file(s) associated with the real-world object based on the extracted data, in some embodiments. In other or similar embodiments, platform120can extract, from one or more outputs from GM620, an updated model file that reflects the updates to the rendering the real-world object.

In an illustrative example, the real-world object can be a jacket that is to be worn by a character in a video game environment. Platform120can generate and/or update one or more model files associated with generating a rendering of an animation of the jacket (and/or the character wearing the jacket), as described herein. A designer and/or developer can provide via QT601a query to update the rendering of the jacket to include customized piping on the sleeves of the jacket. The query can be provided in a natural language to the designer and/or the developer (e.g., “Update the rendering of the jacket to include piping on the sleeves”). The query can be provided to GM620and platform120can obtain one or more outputs of GM620(e.g., data reflecting the update to the rendering and/or a model file reflecting the update).

Inference and Training Logic

FIG.8Aillustrates hardware structure(s)815for inference and/or training logic used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic are provided below in conjunction withFIGS.8A and/or8B.

In at least one embodiment, hardware structure(s)815for inference and/or training logic may include, without limitation, a code and/or data storage805to store backward and/or output weight and/or input/output data corresponding to neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, code and/or data storage805stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during backward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, training logic may include, or be coupled to code and/or data storage805to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs)). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, any portion of code and/or data storage805may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of code and/or data storage805may be internal or external to on one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or data storage805may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or data storage805is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors.

In at least one embodiment, code and/or data storage801and code and/or data storage805may be separate storage structures. In at least one embodiment, code and/or data storage801and code and/or data storage805may be same storage structure. In at least one embodiment, code and/or data storage801and code and/or data storage805may be partially same storage structure and partially separate storage structures. In at least one embodiment, any portion of code and/or data storage801and code and/or data storage805may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, hardware structure(s)815for inference and/or training logic may include, without limitation, one or more arithmetic logic unit(s) (“ALU(s)”)810, including integer and/or floating point units, to perform logical and/or mathematical operations based, at least in part on, or indicated by, training and/or inference code (e.g., graph code), a result of which may produce activations (e.g., output values from layers or neurons within a neural network) stored in an activation storage820that are functions of input/output and/or weight parameter data stored in code and/or data storage801and/or code and/or data storage805. In at least one embodiment, activations stored in activation storage820are generated according to linear algebraic and or matrix-based mathematics performed by ALU(s)810in response to performing instructions or other code, wherein weight values stored in code and/or data storage805and/or code and/or data storage801are used as operands along with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, any or all of which may be stored in code and/or data storage805or code and/or data storage801or another storage on or off-chip.

In at least one embodiment, ALU(s)810are included within one or more processors or other hardware logic devices or circuits, whereas in another embodiment, ALU(s)810may be external to a processor or other hardware logic device or circuit that uses them (e.g., a co-processor). In at least one embodiment, ALUs810may be included within a processor's execution units or otherwise within a bank of ALUs accessible by a processor's execution units either within same processor or distributed between different processors of different types (e.g., central processing units, graphics processing units, fixed function units, etc.). In at least one embodiment, code and/or data storage801, code and/or data storage805, and activation storage820may be on same processor or other hardware logic device or circuit, whereas in another embodiment, they may be in different processors or other hardware logic devices or circuits, or some combination of same and different processors or other hardware logic devices or circuits. In at least one embodiment, any portion of activation storage820may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. Furthermore, inferencing and/or training code may be stored with other code accessible to a processor or other hardware logic or circuit and fetched and/or processed using a processor's fetch, decode, scheduling, execution, retirement and/or other logical circuits.

In at least one embodiment, activation storage820may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, activation storage820may be completely or partially within or external to one or more processors or other logical circuits. In at least one embodiment, choice of whether activation storage820is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. In at least one embodiment, inference and/or training logic described with respect to inFIG.8Amay be used in conjunction with an application-specific integrated circuit (“ASIC”), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic may be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware, such as field programmable gate arrays (“FPGAs”).

FIG.8Billustrates hardware structure(s)815for inference and/or training logic, according to at least one or more embodiments. In at least one embodiment, hardware structure(s)815may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used in conjunction with weight values or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, inference and/or training logic may be used in conjunction with an application-specific integrated circuit (ASIC), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic may be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware or other hardware, such as field programmable gate arrays (FPGAs). In at least one embodiment, hardware structure(s)815for inference and/or training logic includes, without limitation, code and/or data storage801and code and/or data storage805, which may be used to store code (e.g., graph code), weight values and/or other information, including bias values, gradient information, momentum values, and/or other parameter or hyperparameter information. In at least one embodiment illustrated inFIG.8B, each of code and/or data storage801and code and/or data storage805is associated with a dedicated computational resource, such as computational hardware802and computational hardware806, respectively. In at least one embodiment, each of computational hardware802and computational hardware806comprises one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in code and/or data storage801and code and/or data storage805, respectively, result of which is stored in activation storage820.

In at least one embodiment, each of code and/or data storage801and805and corresponding computational hardware802and806, respectively, correspond to different layers of a neural network, such that resulting activation from one “storage/computational pair801/802” of code and/or data storage801and computational hardware802is provided as an input to “storage/computational pair805/806” of code and/or data storage805and computational hardware806, in order to mirror conceptual organization of a neural network. In at least one embodiment, each of storage/computational pairs801/702and805/806may correspond to more than one neural network layer. In at least one embodiment, additional storage/computation pairs (not shown) subsequent to or in parallel with storage computation pairs801/802and805/806may be included in inference and/or training logic.

Data Center

FIG.9illustrates an example data center900, in which at least one embodiment may be used. In at least one embodiment, data center900includes a data center infrastructure layer910, a framework layer920, a software layer930, and an application layer940.

In at least one embodiment, as shown inFIG.9, data center infrastructure layer910may include a resource orchestrator912, grouped computing resources914, and node computing resources (“node C.R.s”)916(1)-1016(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s916(1)-1016(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field programmable gate arrays (FPGAs), data processing units, graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s from among node C.R.s916(1)-1016(N) may be a server having one or more of above-mentioned computing resources.

In at least one embodiment, resource orchestrator912may configure or otherwise control one or more node C.R.s916(1)-1016(N) and/or grouped computing resources914. In at least one embodiment, resource orchestrator912may include a software design infrastructure (“SDI”) management entity for data center900. In at least one embodiment, resource orchestrator may include hardware, software or some combination thereof.

In at least one embodiment, as shown inFIG.9, framework layer920includes a job scheduler922, a configuration manager924, a resource manager926and a distributed file system928. In at least one embodiment, framework layer920may include a framework to support software932of software layer930and/or one or more application(s)942of application layer940. In at least one embodiment, software932or application(s)942may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. In at least one embodiment, framework layer920may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system928for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler922may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center900. In at least one embodiment, configuration manager924may be capable of configuring different layers such as software layer930and framework layer920including Spark and distributed file system928for supporting large-scale data processing. In at least one embodiment, resource manager926may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system928and job scheduler922. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource914at data center infrastructure layer910. In at least one embodiment, resource manager926may coordinate with resource orchestrator912to manage these mapped or allocated computing resources.

In at least one embodiment, software932included in software layer930may include software used by at least portions of node C.R.s916(1)-1016(N), grouped computing resources914, and/or distributed file system928of framework layer920. The one or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s)942included in application layer940may include one or more types of applications used by at least portions of node C.R.s916(1)-1016(N), grouped computing resources914, and/or distributed file system928of framework layer920. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager924, resource manager926, and resource orchestrator912may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. In at least one embodiment, self-modifying actions may relieve a data center operator of data center900from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

In at least one embodiment, data center900may include tools, services, software, or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources described above with respect to data center900. In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to data center900by using weight parameters calculated through one or more training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

Computer Systems

In at least one embodiment, computer system1000may include, without limitation, processor1002that may include, without limitation, one or more execution units1008to perform machine learning model training and/or inferencing according to techniques described herein. In at least one embodiment, computer system1000is a single processor desktop or server system, but in another embodiment computer system1000may be a multiprocessor system. In at least one embodiment, processor1002may include, without limitation, a complex instruction set computer (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) microprocessor, a processor implementing a combination of instruction sets, or any other processor device, such as a digital signal processor, for example. In at least one embodiment, processor1002may be coupled to a processor bus1010that may transmit data signals between processor1002and other components in computer system1000.

In at least one embodiment, processor1002may include, without limitation, a Level 1 (“L1”) internal cache memory (“cache”)1004. In at least one embodiment, processor1002may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may reside external to processor1002. Other embodiments may also include a combination of both internal and external caches depending on particular implementation and needs. In at least one embodiment, register file1006may store different types of data in various registers including, without limitation, integer registers, floating point registers, status registers, and instruction pointer register.

In at least one embodiment, execution unit1008, including, without limitation, logic to perform integer and floating point operations, also resides in processor1002. In at least one embodiment, processor1002may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions. In at least one embodiment, execution unit1008may include logic to handle a packed instruction set1009. In at least one embodiment, by including packed instruction set1009in an instruction set of a general-purpose processor1002, along with associated circuitry to execute instructions, operations used by many multimedia applications may be performed using packed data in a general-purpose processor1002. In one or more embodiments, many multimedia applications may be accelerated and executed more efficiently by using full width of a processor's data bus for performing operations on packed data, which may eliminate need to transfer smaller units of data across processor's data bus to perform one or more operations one data element at a time.

In at least one embodiment, execution unit1008may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, computer system1000may include, without limitation, a memory1020. In at least one embodiment, memory1020may be implemented as a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, flash memory device, or other memory device. In at least one embodiment, memory1020may store instruction(s)1019and/or data1021represented by data signals that may be executed by processor1002.

In at least one embodiment, system logic chip may be coupled to processor bus1010and memory1020. In at least one embodiment, system logic chip may include, without limitation, a memory controller hub (“MCH”)1016, and processor1002may communicate with MCH1016via processor bus1010. In at least one embodiment, MCH1016may provide a high bandwidth memory path1018to memory1020for instruction and data storage and for storage of graphics commands, data and textures. In at least one embodiment, MCH1016may direct data signals between processor1002, memory1020, and other components in computer system1000and to bridge data signals between processor bus1010, memory1020, and a system I/O1022. In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH1016may be coupled to memory1020through a high bandwidth memory path1018and graphics/video card1012may be coupled to MCH1016through an Accelerated Graphics Port (“AGP”) interconnect1014.

In at least one embodiment, computer system1000may use system I/O1022that is a proprietary hub interface bus to couple MCH1016to I/O controller hub (“ICH”)1030. In at least one embodiment, ICH1030may provide direct connections to some I/O devices via a local I/O bus. In at least one embodiment, local I/O bus may include, without limitation, a high-speed I/O bus for connecting peripherals to memory1020, chipset, and processor1002. Examples may include, without limitation, an audio controller1029, a firmware hub (“flash BIOS”)1028, a wireless transceiver1026, a data storage1024, a legacy I/O controller1023containing user input and keyboard interfaces1025, a serial expansion port1027, such as Universal Serial Bus (“USB”), and a network controller1034, which may include in some embodiments, a data processing unit. Data storage1024may comprise a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device.

In at least one embodiment,FIG.10illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,FIG.10may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of computer system1000are interconnected using compute express link (CXL) interconnects.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.11is a block diagram illustrating an electronic device1100for utilizing a processor1110, according to at least one embodiment. In at least one embodiment, electronic device1100may be, for example and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, an edge device, an IoT device, or any other suitable electronic device.

In at least one embodiment, system1100may include, without limitation, processor1110communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, processor1110coupled using a bus or interface, such as a 1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus, a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”) bus, a Serial Advance Technology Attachment (“SATA”) bus, a Universal Serial Bus (“USB”) (versions 1, 2, 3), or a Universal Asynchronous Receiver/Transmitter (“UART”) bus. In at least one embodiment,FIG.11illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,FIG.11may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices illustrated inFIG.11may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components ofFIG.11are interconnected using compute express link (CXL) interconnects.

In at least one embodiment,FIG.11may include a display1124, a touch screen1125, a touch pad1130, a Near Field Communications unit (“NFC”)1145, a sensor hub1140, a thermal sensor1146, an Express Chipset (“EC”)1135, a Trusted Platform Module (“TPM”)1138, BIOS/firmware/flash memory (“BIOS, FW Flash”)1122, a DSP1160, a drive1120such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local area network unit (“WLAN”)1150, a Bluetooth unit1152, a Wireless Wide Area Network unit (“WWAN”)1156, a Global Positioning System (GPS)1155, a camera (“USB 3.0 camera”)1154such as a USB 3.0 camera, and/or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”)1115implemented in, for example, LPDDR3 standard. These components may each be implemented in any suitable manner.

In at least one embodiment, other components may be communicatively coupled to processor1110through components discussed above. In at least one embodiment, an accelerometer1141, Ambient Light Sensor (“ALS”)1142, compass1143, and a gyroscope1144may be communicatively coupled to sensor hub1140. In at least one embodiment, thermal sensor1139, a fan1137, a keyboard1136, and a touch pad1130may be communicatively coupled to EC1135. In at least one embodiment, speaker1163, headphones1164, and microphone (“mic”)1165may be communicatively coupled to an audio unit (“audio codec and class d amp”)1162, which may in turn be communicatively coupled to DSP1160. In at least one embodiment, audio unit1164may include, for example and without limitation, an audio coder/decoder (“codec”) and a class D amplifier. In at least one embodiment, SIM card (“SIM”)1157may be communicatively coupled to WWAN unit1156. In at least one embodiment, components such as WLAN unit1150and Bluetooth unit1152, as well as WWAN unit1156may be implemented in a Next Generation Form Factor (“NGFF”).

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.12is a block diagram of a processing system, according to at least one embodiment. In at least one embodiment, system1200includes one or more processors1202and one or more graphics processors1208, and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors1202or processor cores1207. In at least one embodiment, system1200is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, edge, or embedded devices.

In at least one embodiment, system1200may include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In at least one embodiment, system1200is a mobile phone, smart phone, tablet computing device or mobile Internet device. In at least one embodiment, processing system1200may also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In at least one embodiment, processing system1200is a television or set top box device having one or more processors1202and a graphical interface generated by one or more graphics processors1208.

In at least one embodiment, one or more processors1202each include one or more processor cores1207to process instructions which, when executed, perform operations for system and user software. In at least one embodiment, each of one or more processor cores1207is configured to process a specific instruction set1209. In at least one embodiment, instruction set1209may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). In at least one embodiment, processor cores1207may each process a different instruction set1209, which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core1207may also include other processing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor1202includes cache memory1204. In at least one embodiment, processor1202may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared among various components of processor1202. In at least one embodiment, processor1202also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores1207using known cache coherency techniques. In at least one embodiment, register file1206is additionally included in processor1202which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file1206may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s)1202are coupled with one or more interface bus(es)1210to transmit communication signals such as address, data, or control signals between processor1202and other components in system1200. In at least one embodiment, interface bus1210, in one embodiment, may be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, interface1210is not limited to a DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In at least one embodiment processor(s)1202include an integrated memory controller1216and a platform controller hub1230. In at least one embodiment, memory controller1216facilitates communication between a memory device and other components of system1200, while platform controller hub (PCH)1230provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device1220may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In at least one embodiment memory device1220may operate as system memory for system1200, to store data1222and instructions1221for use when one or more processors1202executes an application or process. In at least one embodiment, memory controller1216also couples with an optional external graphics processor1212, which may communicate with one or more graphics processors1208in processors1202to perform graphics and media operations. In at least one embodiment, a display device1211may connect to processor(s)1202. In at least one embodiment display device1211may include one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, display device1211may include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.

In at least one embodiment, platform controller hub1230enables peripherals to connect to memory device1220and processor1202via a high-speed I/O bus. In at least one embodiment, I/O peripherals include, but are not limited to, an audio controller1246, a network controller1234, a firmware interface1228, a wireless transceiver1226, touch sensors1225, a data storage device1224(e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device1224may connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors1225may include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver1226may be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at least one embodiment, firmware interface1228enables communication with system firmware, and may be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller1234may enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) couples with interface bus1210. In at least one embodiment, audio controller1246is a multi-channel high definition audio controller. In at least one embodiment, system1200includes an optional legacy I/O controller1240for coupling legacy (e.g., Personal System2(PS/2)) devices to system. In at least one embodiment, platform controller hub1230may also connect to one or more Universal Serial Bus (USB) controllers1242connect input devices, such as keyboard and mouse1243combinations, a camera1244, or other USB input devices.

In at least one embodiment, an instance of memory controller1216and platform controller hub1230may be integrated into a discreet external graphics processor, such as external graphics processor1212. In at least one embodiment, platform controller hub1230and/or memory controller1216may be external to one or more processor(s)1202. For example, in at least one embodiment, system1200may include an external memory controller1216and platform controller hub1230, which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with processor(s)1202.

Inference and/or training logic of hardware structure(s)815are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic of hardware structure(s)815are provided herein in conjunction withFIGS.8A and/or8B. In at least one embodiment portions or all of inference and/or training logic of hardware structure(s)815may be incorporated into graphics processor1300. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in a graphics processor. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated inFIG.8A or8B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of a graphics processor to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.13is a block diagram of a processor1300having one or more processor cores1302A-1402N, an integrated memory controller1314, and an integrated graphics processor1308, according to at least one embodiment. In at least one embodiment, processor1300may include additional cores up to and including additional core1302N represented by dashed lined boxes. In at least one embodiment, each of processor cores1302A-1402N includes one or more internal cache units1304A-1404N. In at least one embodiment, each processor core also has access to one or more shared cached units1306.

In at least one embodiment, internal cache units1304A-1404N and shared cache units1306represent a cache memory hierarchy within processor1300. In at least one embodiment, cache memory units1304A-1404N may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where a highest level of cache before external memory is classified as an LLC. In at least one embodiment, cache coherency logic maintains coherency between various cache units1306and1304A-1404N.

In at least one embodiment, processor1300may also include a set of one or more bus controller units1316and a system agent core1310. In at least one embodiment, one or more bus controller units1316manage a set of peripheral buses, such as one or more PCI or PCI express busses. In at least one embodiment, system agent core1310provides management functionality for various processor components. In at least one embodiment, system agent core1310includes one or more integrated memory controllers1314to manage access to various external memory devices (not shown).

In at least one embodiment, one or more of processor cores1302A-1402N include support for simultaneous multi-threading. In at least one embodiment, system agent core1310includes components for coordinating and operating cores1302A-1402N during multi-threaded processing. In at least one embodiment, system agent core1310may additionally include a power control unit (PCU), which includes logic and components to regulate one or more power states of processor cores1302A-1402N and graphics processor1308.

In at least one embodiment, processor1300additionally includes graphics processor1308to execute graphics processing operations. In at least one embodiment, graphics processor1308couples with shared cache units1306, and system agent core1310, including one or more integrated memory controllers1314. In at least one embodiment, system agent core1310also includes a display controller1311to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller1311may also be a separate module coupled with graphics processor1308via at least one interconnect, or may be integrated within graphics processor1308.

In at least one embodiment, a ring based interconnect unit1312is used to couple internal components of processor1300. In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor1308couples with ring interconnect1312via an I/O link1313.

In at least one embodiment, I/O link1313represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module1318, such as an eDRAM module. In at least one embodiment, each of processor cores1302A-1402N and graphics processor1308use embedded memory modules1318as a shared Last Level Cache.

In at least one embodiment, processor cores1302A-1402N are homogenous cores executing a common instruction set architecture. In at least one embodiment, processor cores1302A-1402N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores1302A-1402N execute a common instruction set, while one or more other cores of processor cores1302A-1402N executes a subset of a common instruction set or a different instruction set. In at least one embodiment, processor cores1302A-1402N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In at least one embodiment, processor1300may be implemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic of hardware structure(s)815are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic of hardware structure(s)815are provided herein in conjunction withFIGS.8A and/or8B. In at least one embodiment portions or all of inference and/or training logic of hardware structure(s)815may be incorporated into processor1300. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor1308, graphics core(s)1302A-1402N, or other components inFIG.13. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated inFIG.8Aor8B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor1300to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

Virtualized Computing Platform

FIG.14is an example data flow diagram for a process1400of generating and deploying an image processing and inferencing pipeline, in accordance with at least one embodiment. In at least one embodiment, process1400may be deployed for use with imaging devices, processing devices, and/or other device types at one or more facilities1402. Process1400may be executed within a training system1404and/or a deployment system1406. In at least one embodiment, training system1404may be used to perform training, deployment, and implementation of machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system1406. In at least one embodiment, deployment system1406may be configured to offload processing and compute resources among a distributed computing environment to reduce infrastructure requirements at facility1402. In at least one embodiment, one or more applications in a pipeline may use or call upon services (e.g., inference, visualization, compute, AI, etc.) of deployment system1406during execution of applications.

In at least one embodiment, some of applications used in advanced processing and inferencing pipelines may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models may be trained at facility1402using data1408(such as imaging data) generated at facility1402(and stored on one or more picture archiving and communication system (PACS) servers at facility1402), may be trained using imaging or sequencing data1408from another facility(ies), or a combination thereof. In at least one embodiment, training system1404may be used to provide applications, services, and/or other resources for generating working, deployable machine learning models for deployment system1406.

In at least one embodiment, model registry1424may be backed by object storage that may support versioning and object metadata. In at least one embodiment, object storage may be accessible through, for example, a cloud storage (e.g., cloud1526ofFIG.15) compatible application programming interface (API) from within a cloud platform. In at least one embodiment, machine learning models within model registry1424may uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API. In at least one embodiment, an API may provide access to methods that allow users with appropriate credentials to associate models with applications, such that models may be executed as part of execution of containerized instantiations of applications.

In at least one embodiment, training pipeline1504(FIG.15) may include a scenario where facility1402is training their own machine learning model, or has an existing machine learning model that needs to be optimized or updated. In at least one embodiment, imaging data1408generated by imaging device(s), sequencing devices, and/or other device types may be received. In at least one embodiment, once imaging data1408is received, AI-assisted annotation1410may be used to aid in generating annotations corresponding to imaging data1408to be used as ground truth data for a machine learning model. In at least one embodiment, AI-assisted annotation1410may include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that may be trained to generate annotations corresponding to certain types of imaging data1408(e.g., from certain devices). In at least one embodiment, AI-assisted annotations1410may then be used directly, or may be adjusted or fine-tuned using an annotation tool to generate ground truth data. In at least one embodiment, AI-assisted annotations1410, labeled clinic data1412, or a combination thereof may be used as ground truth data for training a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model1416, and may be used by deployment system1406, as described herein.

In at least one embodiment, training pipeline1504(FIG.15) may include a scenario where facility1402needs a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system1406, but facility1402may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, an existing machine learning model may be selected from a model registry1424. In at least one embodiment, model registry1424may include machine learning models trained to perform a variety of different inference tasks on imaging data. In at least one embodiment, machine learning models in model registry1424may have been trained on imaging data from different facilities than facility1402(e.g., facilities remotely located). In at least one embodiment, machine learning models may have been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when being trained on imaging data from a specific location, training may take place at that location, or at least in a manner that protects confidentiality of imaging data or restricts imaging data from being transferred off-premises. In at least one embodiment, once a model is trained—or partially trained—at one location, a machine learning model may be added to model registry1424. In at least one embodiment, a machine learning model may then be retrained, or updated, at any number of other facilities, and a retrained or updated model may be made available in model registry1424. In at least one embodiment, a machine learning model may then be selected from model registry1424—and referred to as output model1416—and may be used in deployment system1406to perform one or more processing tasks for one or more applications of a deployment system.

In at least one embodiment, training pipeline1504(FIG.15), a scenario may include facility1402requiring a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system1406, but facility1402may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, a machine learning model selected from model registry1424may not be fine-tuned or optimized for imaging data1408generated at facility1402because of differences in populations, robustness of training data used to train a machine learning model, diversity in anomalies of training data, and/or other issues with training data. In at least one embodiment, AI-assisted annotation1410may be used to aid in generating annotations corresponding to imaging data1408to be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, labeled data1412may be used as ground truth data for training a machine learning model. In at least one embodiment, retraining or updating a machine learning model may be referred to as model training1414. In at least one embodiment, model training1414—e.g., AI-assisted annotations1410, labeled clinic data1412, or a combination thereof—may be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model1416, and may be used by deployment system1406, as described herein.

In at least one embodiment, deployment system1406may include software1418, services1420, hardware1422, and/or other components, features, and functionality. In at least one embodiment, deployment system1406may include a software “stack,” such that software1418may be built on top of services1420and may use services1420to perform some or all of processing tasks, and services1420and software1418may be built on top of hardware1422and use hardware1422to execute processing, storage, and/or other compute tasks of deployment system1406. In at least one embodiment, software1418may include any number of different containers, where each container may execute an instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks in an advanced processing and inferencing pipeline (e.g., inferencing, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data1408, in addition to containers that receive and configure imaging data for use by each container and/or for use by facility1402after processing through a pipeline (e.g., to convert outputs back to a usable data type). In at least one embodiment, a combination of containers within software1418(e.g., that make up a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and a virtual instrument may leverage services1420and hardware1422to execute some or all processing tasks of applications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive input data (e.g., imaging data1408) in a specific format in response to an inference request (e.g., a request from a user of deployment system1406). In at least one embodiment, input data may be representative of one or more images, video, and/or other data representations generated by one or more imaging devices. In at least one embodiment, data may undergo pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare an output data for a next application and/or to prepare output data for transmission and/or use by a user (e.g., as a response to an inference request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output models1416of training system1404.

In at least one embodiment, tasks of data processing pipeline may be encapsulated in a container(s) that each represents a discrete, fully functional instantiation of an application and virtualized computing environment that is able to reference machine learning models. In at least one embodiment, containers or applications may be published into a private (e.g., limited access) area of a container registry (described in more detail herein), and trained or deployed models may be stored in model registry1424and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container registry, and once selected by a user from a container registry for deployment in a pipeline, an image may be used to generate a container for an instantiation of an application for use by a user's system.

In at least one embodiment, developers (e.g., software developers, clinicians, doctors, etc.) may develop, publish, and store applications (e.g., as containers) for performing image processing and/or inferencing on supplied data. In at least one embodiment, development, publishing, and/or storing may be performed using a software development kit (SDK) associated with a system (e.g., to ensure that an application and/or container developed is compliant with or compatible with a system). In at least one embodiment, an application that is developed may be tested locally (e.g., at a first facility, on data from a first facility) with an SDK which may support at least some of services1420as a system (e.g., system1500ofFIG.15). In at least one embodiment, because DICOM objects may contain anywhere from one to hundreds of images or other data types, and due to a variation in data, a developer may be responsible for managing (e.g., setting constructs for, building pre-processing into an application, etc.) extraction and preparation of incoming data. In at least one embodiment, once validated by system1500(e.g., for accuracy), an application may be available in a container registry for selection and/or implementation by a user to perform one or more processing tasks with respect to data at a facility (e.g., a second facility) of a user.

In at least one embodiment, developers may then share applications or containers through a network for access and use by users of a system (e.g., system1500ofFIG.15). In at least one embodiment, completed and validated applications or containers may be stored in a container registry and associated machine learning models may be stored in model registry1424. In at least one embodiment, a requesting entity—who provides an inference or image processing request—may browse a container registry and/or model registry1424for an application, container, dataset, machine learning model, etc., select a desired combination of elements for inclusion in data processing pipeline, and submit an imaging processing request. In at least one embodiment, a request may include input data (and associated patient data, in some examples) that is necessary to perform a request, and/or may include a selection of application(s) and/or machine learning models to be executed in processing a request. In at least one embodiment, a request may then be passed to one or more components of deployment system1406(e.g., a cloud) to perform processing of data processing pipeline. In at least one embodiment, processing by deployment system1406may include referencing selected elements (e.g., applications, containers, models, etc.) from a container registry and/or model registry1424. In at least one embodiment, once results are generated by a pipeline, results may be returned to a user for reference (e.g., for viewing in a viewing application suite executing on a local, on-premises workstation or terminal).

In at least one embodiment, to aid in processing or execution of applications or containers in pipelines, services1420may be leveraged. In at least one embodiment, services1420may include compute services, artificial intelligence (AI) services, visualization services, and/or other service types. In at least one embodiment, services1420may provide functionality that is common to one or more applications in software1418, so functionality may be abstracted to a service that may be called upon or leveraged by applications. In at least one embodiment, functionality provided by services1420may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform1530(FIG.15)). In at least one embodiment, rather than each application that shares a same functionality offered by a service1420being required to have a respective instance of service1420, service1420may be shared between and among various applications. In at least one embodiment, services may include an inference server or engine that may be used for executing detection or segmentation tasks, as non-limiting examples. In at least one embodiment, a model training service may be included that may provide machine learning model training and/or retraining capabilities. In at least one embodiment, a data augmentation service may further be included that may provide GPU accelerated data (e.g., DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing, scaling, and/or other augmentation. In at least one embodiment, a visualization service may be used that may add image rendering effects—such as ray-tracing, rasterization, denoising, sharpening, etc.—to add realism to two-dimensional (2D) and/or three-dimensional (3D) models. In at least one embodiment, virtual instrument services may be included that provide for beam-forming, segmentation, inferencing, imaging, and/or support for other applications within pipelines of virtual instruments.

In at least one embodiment, where a service1420includes an AI service (e.g., an inference service), one or more machine learning models may be executed by calling upon (e.g., as an API call) an inference service (e.g., an inference server) to execute machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, where another application includes one or more machine learning models for segmentation tasks, an application may call upon an inference service to execute machine learning models for performing one or more of processing operations associated with segmentation tasks. In at least one embodiment, software1418implementing advanced processing and inferencing pipeline that includes segmentation application and anomaly detection application may be streamlined because each application may call upon a same inference service to perform one or more inferencing tasks.

In at least one embodiment, hardware1422may include GPUs, CPUs, DPUs, graphics cards, an AI/deep learning system (e.g., an AI supercomputer, such as NVIDIA's DGX), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware1422may be used to provide efficient, purpose-built support for software1418and services1420in deployment system1406. In at least one embodiment, use of GPU processing may be implemented for processing locally (e.g., at facility1402), within an AI/deep learning system, in a cloud system, and/or in other processing components of deployment system1406to improve efficiency, accuracy, and efficacy of image processing and generation. In at least one embodiment, software1418and/or services1420may be optimized for GPU processing with respect to deep learning, machine learning, and/or high-performance computing, as non-limiting examples. In at least one embodiment, at least some of computing environment of deployment system1406and/or training system1404may be executed in a datacenter one or more supercomputers or high performance computing systems, with GPU optimized software (e.g., hardware and software combination of NVIDIA's DGX System). In at least one embodiment, hardware1422may include any number of GPUs that may be called upon to perform processing of data in parallel, as described herein. In at least one embodiment, cloud platform may further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks. In at least one embodiment, cloud platform may further include DPU processing to transmit data received over a network and/or through a network controller or other network interface directly to (e.g., a memory of) one or more GPU(s). In at least one embodiment, cloud platform (e.g., NVIDIA's NGC) may be executed using an AI/deep learning supercomputer(s) and/or GPU-optimized software (e.g., as provided on NVIDIA's DGX Systems) as a hardware abstraction and scaling platform. In at least one embodiment, cloud platform may integrate an application container clustering system or orchestration system (e.g., KUBERNETES) on multiple GPUs to enable seamless scaling and load balancing.

FIG.15is a system diagram for an example system1500for generating and deploying an imaging deployment pipeline, in accordance with at least one embodiment. In at least one embodiment, system1500may be used to implement process1400ofFIG.14and/or other processes including advanced processing and inferencing pipelines. In at least one embodiment, system1500may include training system1404and deployment system1406. In at least one embodiment, training system1404and deployment system1406may be implemented using software1418, services1420, and/or hardware1422, as described herein.

In at least one embodiment, system1500(e.g., training system1404and/or deployment system1406) may implemented in a cloud computing environment (e.g., using cloud1526). In at least one embodiment, system1500may be implemented locally with respect to a healthcare services facility, or as a combination of both cloud and local computing resources. In at least one embodiment, access to APIs in cloud1526may be restricted to authorized users through enacted security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriate authorization. In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of system1500, may be restricted to a set of public IPs that have been vetted or authorized for interaction.

In at least one embodiment, various components of system1500may communicate between and among one another using any of a variety of different network types, including but not limited to local area networks (LANs) and/or wide area networks (WANs) via wired and/or wireless communication protocols. In at least one embodiment, communication between facilities and components of system1500(e.g., for transmitting inference requests, for receiving results of inference requests, etc.) may be communicated over data bus(ses), wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

In at least one embodiment, training system1404may execute training pipelines1504, similar to those described herein with respect toFIG.14. In at least one embodiment, where one or more machine learning models are to be used in deployment pipelines1510by deployment system1406, training pipelines1504may be used to train or retrain one or more (e.g., pre-trained) models, and/or implement one or more of pre-trained models1506(e.g., without a need for retraining or updating). In at least one embodiment, as a result of training pipelines1504, output model(s)1416may be generated. In at least one embodiment, training pipelines1504may include any number of processing steps, such as but not limited to imaging data (or other input data) conversion or adaption In at least one embodiment, for different machine learning models used by deployment system1406, different training pipelines1504may be used. In at least one embodiment, training pipeline1504similar to a first example described with respect toFIG.14may be used for a first machine learning model, training pipeline1504similar to a second example described with respect toFIG.14may be used for a second machine learning model, and training pipeline1504similar to a third example described with respect toFIG.14may be used for a third machine learning model. In at least one embodiment, any combination of tasks within training system1404may be used depending on what is required for each respective machine learning model. In at least one embodiment, one or more of machine learning models may already be trained and ready for deployment so machine learning models may not undergo any processing by training system1404, and may be implemented by deployment system1406.

In at least one embodiment, output model(s)1416and/or pre-trained model(s)1506may include any types of machine learning models depending on implementation or embodiment.

In at least one embodiment, training pipelines1504may include AI-assisted annotation, as described in more detail herein with respect to at leastFIG.16B. In at least one embodiment, labeled data1412(e.g., traditional annotation) may be generated by any number of techniques. In at least one embodiment, labels or other annotations may be generated within a drawing program (e.g., an annotation program), a computer aided design (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground truth, and/or may be hand drawn, in some examples. In at least one embodiment, ground truth data may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., labeler, or annotation expert, defines location of labels), and/or a combination thereof. In at least one embodiment, for each instance of imaging data1408(or other data type used by machine learning models), there may be corresponding ground truth data generated by training system1404. In at least one embodiment, AI-assisted annotation may be performed as part of deployment pipelines1510; either in addition to, or in lieu of AI-assisted annotation included in training pipelines1504. In at least one embodiment, system1500may include a multi-layer platform that may include a software layer (e.g., software1418) of diagnostic applications (or other application types) that may perform one or more medical imaging and diagnostic functions. In at least one embodiment, system1500may be communicatively coupled to (e.g., via encrypted links) PACS server networks of one or more facilities. In at least one embodiment, system1500may be configured to access and referenced data from PACS servers to perform operations, such as training machine learning models, deploying machine learning models, image processing, inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as a secure, encrypted, and/or authenticated API through which applications or containers may be invoked (e.g., called) from an external environment(s) (e.g., facility1402). In at least one embodiment, applications may then call or execute one or more services1420for performing compute, AI, or visualization tasks associated with respective applications, and software1418and/or services1420may leverage hardware1422to perform processing tasks in an effective and efficient manner.

In at least one embodiment, deployment system1406may execute deployment pipelines1510. In at least one embodiment, deployment pipelines1510may include any number of applications that may be sequentially, non-sequentially, or otherwise applied to imaging data (and/or other data types) generated by imaging devices, sequencing devices, genomics devices, etc.—including AI-assisted annotation, as described above. In at least one embodiment, as described herein, a deployment pipeline1510for an individual device may be referred to as a virtual instrument for a device (e.g., a virtual ultrasound instrument, a virtual CT scan instrument, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, there may be more than one deployment pipeline1510depending on information desired from data generated by a device. In at least one embodiment, where detections of anomalies are desired from an MRI machine, there may be a first deployment pipeline1510, and where image enhancement is desired from output of an MRI machine, there may be a second deployment pipeline1510.

In at least one embodiment, an image generation application may include a processing task that includes use of a machine learning model. In at least one embodiment, a user may desire to use their own machine learning model, or to select a machine learning model from model registry1424. In at least one embodiment, a user may implement their own machine learning model or select a machine learning model for inclusion in an application for performing a processing task. In at least one embodiment, applications may be selectable and customizable, and by defining constructs of applications, deployment, and implementation of applications for a particular user are presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system1500—such as services1420and hardware1422—deployment pipelines1510may be even more user friendly, provide for easier integration, and produce more accurate, efficient, and timely results.

In at least one embodiment, deployment system1406may include a user interface1514(e.g., a graphical user interface, a web interface, etc.) that may be used to select applications for inclusion in deployment pipeline(s)1510, arrange applications, modify, or change applications or parameters or constructs thereof, use and interact with deployment pipeline(s)1510during set-up and/or deployment, and/or to otherwise interact with deployment system1406. In at least one embodiment, although not illustrated with respect to training system1404, user interface1514(or a different user interface) may be used for selecting models for use in deployment system1406, for selecting models for training, or retraining, in training system1404, and/or for otherwise interacting with training system1404.

In at least one embodiment, pipeline manager1512may be used, in addition to an application orchestration system1528, to manage interaction between applications or containers of deployment pipeline(s)1510and services1420and/or hardware1422. In at least one embodiment, pipeline manager1512may be configured to facilitate interactions from application to application, from application to service1420, and/or from application or service to hardware1422. In at least one embodiment, although illustrated as included in software1418, this is not intended to be limiting, and in some examples (e.g., as illustrated inFIG.13) pipeline manager1512may be included in services1420. In at least one embodiment, application orchestration system1528(e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that may group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from deployment pipeline(s)1510(e.g., a reconstruction application, a segmentation application, etc.) with individual containers, each application may execute in a self-contained environment (e.g., at a kernel level) to increase speed and efficiency.

In at least one embodiment, each application and/or container (or image thereof) may be individually developed, modified, and deployed (e.g., a first user or developer may develop, modify, and deploy a first application and a second user or developer may develop, modify, and deploy a second application separate from a first user or developer), which may allow for focus on, and attention to, a task of a single application and/or container(s) without being hindered by tasks of another application(s) or container(s). In at least one embodiment, communication, and cooperation between different containers or applications may be aided by pipeline manager1512and application orchestration system1528. In at least one embodiment, so long as an expected input and/or output of each container or application is known by a system (e.g., based on constructs of applications or containers), application orchestration system1528and/or pipeline manager1512may facilitate communication among and between, and sharing of resources among and between, each of applications or containers. In at least one embodiment, because one or more of applications or containers in deployment pipeline(s)1510may share same services and resources, application orchestration system1528may orchestrate, load balance, and determine sharing of services or resources between and among various applications or containers. In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability. In at least one embodiment, a scheduler may thus allocate resources to different applications and distribute resources between and among applications in view of requirements and availability of a system. In some examples, a scheduler (and/or other component of application orchestration system1528) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of need for data outputs (e.g., to determine whether to execute real-time processing or delayed processing), etc.

In at least one embodiment, services1420leveraged by and shared by applications or containers in deployment system1406may include compute services1516, AI services1518, visualization services1520, and/or other service types. In at least one embodiment, applications may call (e.g., execute) one or more of services1420to perform processing operations for an application. In at least one embodiment, compute services1516may be leveraged by applications to perform super-computing or other high-performance computing (HPC) tasks. In at least one embodiment, compute service(s)1516may be leveraged to perform parallel processing (e.g., using a parallel computing platform1530) for processing data through one or more of applications and/or one or more tasks of a single application, substantially simultaneously. In at least one embodiment, parallel computing platform1530(e.g., NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU) (e.g., GPUs1522). In at least one embodiment, a software layer of parallel computing platform1530may provide access to virtual instruction sets and parallel computational elements of GPUs, for execution of compute kernels. In at least one embodiment, parallel computing platform1530may include memory and, in some embodiments, a memory may be shared between and among multiple containers, and/or between and among different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and/or for multiple processes within a container to use same data from a shared segment of memory of parallel computing platform1530(e.g., where multiple different stages of an application or multiple applications are processing same information). In at least one embodiment, rather than making a copy of data and moving data to different locations in memory (e.g., a read/write operation), same data in same location of a memory may be used for any number of processing tasks (e.g., at a same time, at different times, etc.). In at least one embodiment, as data is used to generate new data as a result of processing, this information of a new location of data may be stored and shared between various applications. In at least one embodiment, location of data and a location of updated or modified data may be part of a definition of how a payload is understood within containers.

In at least one embodiment, AI services1518may be leveraged to perform inferencing services for executing machine learning model(s) associated with applications (e.g., tasked with performing one or more processing tasks of an application). In at least one embodiment, AI services1518may leverage AI system1524to execute machine learning model(s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and/or other inferencing tasks. In at least one embodiment, applications of deployment pipeline(s)1510may use one or more of output models1416from training system1404and/or other models of applications to perform inference on imaging data. In at least one embodiment, two or more examples of inferencing using application orchestration system1528(e.g., a scheduler) may be available. In at least one embodiment, a first category may include a high priority/low latency path that may achieve higher service level agreements, such as for performing inference on urgent requests during an emergency, or for a radiologist during diagnosis. In at least one embodiment, a second category may include a standard priority path that may be used for requests that may be non-urgent or where analysis may be performed at a later time. In at least one embodiment, application orchestration system1528may distribute resources (e.g., services1420and/or hardware1422) based on priority paths for different inferencing tasks of AI services1518.

In at least one embodiment, shared storage may be mounted to AI services1518within system1500. In at least one embodiment, shared storage may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a request may be received by a set of API instances of deployment system1406, and one or more instances may be selected (e.g., for best fit, for load balancing, etc.) to process a request. In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry1424if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache. In at least one embodiment, a scheduler (e.g., of pipeline manager1512) may be used to launch an application that is referenced in a request if an application is not already running or if there are not enough instances of an application. In at least one embodiment, if an inference server is not already launched to execute a model, an inference server may be launched. Any number of inference servers may be launched per model. In at least one embodiment, in a pull model, in which inference servers are clustered, models may be cached whenever load balancing is advantageous. In at least one embodiment, inference servers may be statically loaded in corresponding, distributed servers.

In at least one embodiment, inferencing may be performed using an inference server that runs in a container. In at least one embodiment, an instance of an inference server may be associated with a model (and optionally a plurality of versions of a model). In at least one embodiment, if an instance of an inference server does not exist when a request to perform inference on a model is received, a new instance may be loaded. In at least one embodiment, when starting an inference server, a model may be passed to an inference server such that a same container may be used to serve different models so long as inference server is running as a different instance.

In at least one embodiment, during application execution, an inference request for a given application may be received, and a container (e.g., hosting an instance of an inference server) may be loaded (if not already), and a start procedure may be called. In at least one embodiment, pre-processing logic in a container may load, decode, and/or perform any additional pre-processing on incoming data (e.g., using a CPU(s) and/or GPU(s) and/or DPU(s)). In at least one embodiment, once data is prepared for inference, a container may perform inference as necessary on data. In at least one embodiment, this may include a single inference call on one image (e.g., a hand X-ray), or may require inference on hundreds of images (e.g., a chest CT). In at least one embodiment, an application may summarize results before completing, which may include, without limitation, a single confidence score, pixel level-segmentation, voxel-level segmentation, generating a visualization, or generating text to summarize findings. In at least one embodiment, different models or applications may be assigned different priorities. For example, some models may have a real-time (TAT <1 min) priority while others may have lower priority (e.g., TAT <11 min). In at least one embodiment, model execution times may be measured from requesting institution or entity and may include partner network traversal time, as well as execution on an inference service.

In at least one embodiment, transfer of requests between services1420and inference applications may be hidden behind a software development kit (SDK), and robust transport may be provided through a queue. In at least one embodiment, a request will be placed in a queue via an API for an individual application/tenant ID combination and an SDK will pull a request from a queue and give a request to an application. In at least one embodiment, a name of a queue may be provided in an environment from where an SDK will pick it up. In at least one embodiment, asynchronous communication through a queue may be useful as it may allow any instance of an application to pick up work as it becomes available. Results may be transferred back through a queue, to ensure no data is lost. In at least one embodiment, queues may also provide an ability to segment work, as highest priority work may go to a queue with most instances of an application connected to it, while lowest priority work may go to a queue with a single instance connected to it that processes tasks in an order received. In at least one embodiment, an application may run on a GPU-accelerated instance generated in cloud1526, and an inference service may perform inferencing on a GPU.

In at least one embodiment, visualization services1520may be leveraged to generate visualizations for viewing outputs of applications and/or deployment pipeline(s)1510. In at least one embodiment, GPUs1522may be leveraged by visualization services1520to generate visualizations. In at least one embodiment, rendering effects, such as ray-tracing, may be implemented by visualization services1520to generate higher quality visualizations. In at least one embodiment, visualizations may include, without limitation, 2D image renderings, 3D volume renderings, 3D volume reconstruction, 2D tomographic slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of a system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization services1520may include an internal visualizer, cinematics, and/or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.).

In at least one embodiment, hardware1422may include GPUs1522, AI system1524, cloud1526, and/or any other hardware used for executing training system1404and/or deployment system1406. In at least one embodiment, GPUs1522(e.g., NVIDIA's TESLA and/or QUADRO GPUs) may include any number of GPUs that may be used for executing processing tasks of compute services1516, AI services1518, visualization services1520, other services, and/or any of features or functionality of software1418. For example, with respect to AI services1518, GPUs1522may be used to perform pre-processing on imaging data (or other data types used by machine learning models), post-processing on outputs of machine learning models, and/or to perform inferencing (e.g., to execute machine learning models). In at least one embodiment, cloud1526, AI system1524, and/or other components of system1500may use GPUs1522. In at least one embodiment, cloud1526may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system1524may use GPUs, and cloud1526—or at least a portion tasked with deep learning or inferencing—may be executed using one or more AI systems1524. As such, although hardware1422is illustrated as discrete components, this is not intended to be limiting, and any components of hardware1422may be combined with, or leveraged by, any other components of hardware1422.

In at least one embodiment, AI system1524may include a purpose-built computing system (e.g., a super-computer or an HPC) configured for inferencing, deep learning, machine learning, and/or other artificial intelligence tasks. In at least one embodiment, AI system1524(e.g., NVIDIA's DGX) may include GPU-optimized software (e.g., a software stack) that may be executed using a plurality of GPUs1522, in addition to DPUs, CPUs, RAM, storage, and/or other components, features, or functionality. In at least one embodiment, one or more AI systems1524may be implemented in cloud1526(e.g., in a data center) for performing some or all of AI-based processing tasks of system1500.

In at least one embodiment, cloud1526may include a GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimized platform for executing processing tasks of system1500. In at least one embodiment, cloud1526may include an AI system(s)1524for performing one or more of AI-based tasks of system1500(e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud1526may integrate with application orchestration system1528leveraging multiple GPUs to enable seamless scaling and load balancing between and among applications and services1420. In at least one embodiment, cloud1526may tasked with executing at least some of services1420of system1500, including compute services1516, AI services1518, and/or visualization services1520, as described herein. In at least one embodiment, cloud1526may perform small and large batch inference (e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallel computing API and platform1530(e.g., NVIDIA's CUDA), execute application orchestration system1528(e.g., KUBERNETES), provide a graphics rendering API and platform (e.g., for ray-tracing, 2D graphics, 3D graphics, and/or other rendering techniques to produce higher quality cinematics), and/or may provide other functionality for system1500.

FIG.16Aillustrates a data flow diagram for a process1600to train, retrain, or update a machine learning model, in accordance with at least one embodiment. In at least one embodiment, process1600may be executed using, as a non-limiting example, system1500ofFIG.15. In at least one embodiment, process1600may leverage services1420and/or hardware1422of system1500, as described herein. In at least one embodiment, refined models1612generated by process1600may be executed by deployment system1406for one or more containerized applications in deployment pipelines1510.

In at least one embodiment, model training1414may include retraining or updating an initial model1604(e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset1606, and/or new ground truth data associated with input data). In at least one embodiment, to retrain, or update, initial model1604, output or loss layer(s) of initial model1604may be reset, or deleted, and/or replaced with an updated or new output or loss layer(s). In at least one embodiment, initial model1604may have previously fine-tuned parameters (e.g., weights and/or biases) that remain from prior training, so training or retraining1414may not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training1414, by having reset or replaced output or loss layer(s) of initial model1604, parameters may be updated and re-tuned for a new data set based on loss calculations associated with accuracy of output or loss layer(s) at generating predictions on new, customer dataset1606(e.g., image data1408ofFIG.14).

In at least one embodiment, pre-trained models1506may be stored in a data store, or registry (e.g., model registry1424ofFIG.14). In at least one embodiment, pre-trained models1506may have been trained, at least in part, at one or more facilities other than a facility executing process1600. In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities, pre-trained models1506may have been trained, on-premise, using customer or patient data generated on-premise. In at least one embodiment, pre-trained models1506may be trained using cloud1526and/or other hardware1422, but confidential, privacy protected patient data may not be transferred to, used by, or accessible to any components of cloud1526(or other off premise hardware). In at least one embodiment, where a pre-trained model1506is trained at using patient data from more than one facility, pre-trained model1506may have been individually trained for each facility prior to being trained on patient or customer data from another facility. In at least one embodiment, such as where a customer or patient data has been released of privacy concerns (e.g., by waiver, for experimental use, etc.), or where a customer or patient data is included in a public data set, a customer or patient data from any number of facilities may be used to train pre-trained model1506on-premise and/or off premise, such as in a datacenter or other cloud computing infrastructure.

In at least one embodiment, when selecting applications for use in deployment pipelines1510, a user may also select machine learning models to be used for specific applications. In at least one embodiment, a user may not have a model for use, so a user may select a pre-trained model1506to use with an application. In at least one embodiment, pre-trained model1506may not be optimized for generating accurate results on customer dataset1606of a facility of a user (e.g., based on patient diversity, demographics, types of medical imaging devices used, etc.). In at least one embodiment, prior to deploying pre-trained model1506into deployment pipeline1510for use with an application(s), pre-trained model1506may be updated, retrained, and/or fine-tuned for use at a respective facility.

In at least one embodiment, a user may select pre-trained model1506that is to be updated, retrained, and/or fine-tuned, and pre-trained model1506may be referred to as initial model1604for training system1404within process1600. In at least one embodiment, customer dataset1606(e.g., imaging data, genomics data, sequencing data, or other data types generated by devices at a facility) may be used to perform model training1414(which may include, without limitation, transfer learning) on initial model1604to generate refined model1612. In at least one embodiment, ground truth data corresponding to customer dataset1606may be generated by training system1404. In at least one embodiment, ground truth data may be generated, at least in part, by clinicians, scientists, doctors, practitioners, at a facility (e.g., as labeled clinic data1412ofFIG.14).

In at least one embodiment, AI-assisted annotation1410may be used in some examples to generate ground truth data. In at least one embodiment, AI-assisted annotation1410(e.g., implemented using an AI-assisted annotation SDK) may leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground truth data for a customer dataset. In at least one embodiment, user1610may use annotation tools within a user interface (a graphical user interface (GUI)) on computing device1608.

In at least one embodiment, user1610may interact with a GUI via computing device1608to edit or fine-tune (auto)annotations. In at least one embodiment, a polygon editing feature may be used to move vertices of a polygon to more accurate or fine-tuned locations.

In at least one embodiment, once customer dataset1606has associated ground truth data, ground truth data (e.g., from AI-assisted annotation, manual labeling, etc.) may be used by during model training1414to generate refined model1612. In at least one embodiment, customer dataset1606may be applied to initial model1604any number of times, and ground truth data may be used to update parameters of initial model1604until an acceptable level of accuracy is attained for refined model1612. In at least one embodiment, once refined model1612is generated, refined model1612may be deployed within one or more deployment pipelines1510at a facility for performing one or more processing tasks with respect to medical imaging data.

In at least one embodiment, refined model1612may be uploaded to pre-trained models1506in model registry1424to be selected by another facility. In at least one embodiment, his process may be completed at any number of facilities such that refined model1612may be further refined on new datasets any number of times to generate a more universal model.

FIG.16Bis an example illustration of a client-server architecture1632to enhance annotation tools with pre-trained annotation models, in accordance with at least one embodiment. In at least one embodiment, AI-assisted annotation tools1636may be instantiated based on a client-server architecture1632. In at least one embodiment, annotation tools1636in imaging applications may aid radiologists, for example, identify organs and abnormalities. In at least one embodiment, imaging applications may include software tools that help user1610to identify, as a non-limiting example, a few extreme points on a particular organ of interest in raw images1634(e.g., in a 3D MRI or CT scan) and receive auto-annotated results for all 2D slices of a particular organ. In at least one embodiment, results may be stored in a data store as training data1638and used as (for example and without limitation) ground truth data for training. In at least one embodiment, when computing device1608sends extreme points for AI-assisted annotation1410, a deep learning model, for example, may receive this data as input and return inference results of a segmented organ or abnormality. In at least one embodiment, pre-instantiated annotation tools, such as AI-Assisted Annotation Tool1636B inFIG.16B, may be enhanced by making API calls (e.g., API Call1644) to a server, such as an Annotation Assistant Server1640that may include a set of pre-trained models1642stored in an annotation model registry, for example. In at least one embodiment, an annotation model registry may store pre-trained models1642(e.g., machine learning models, such as deep learning models) that are pre-trained to perform AI-assisted annotation on a particular organ or abnormality. These models may be further updated by using training pipelines1504. In at least one embodiment, pre-installed annotation tools may be improved over time as new labeled clinic data1412is added.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.