PATENT DOCUMENT

Publication Number: US-11797889-B1
Application Number: US-202117557288-A
Country: US
Kind Code: B1

Title: Method and device for modeling a behavior with synthetic training data

Abstract:
In one implementation, a method for modeling a behavior with synthetic training data. The method includes: obtaining source content that includes an entity performing one or more actions within an environment; generating a first environment characterization vector characterizing the environment; generating a first set of behavioral trajectories associated with the one or more actions of the entity based on the source content and the first characterization vector for the environment; generating a second environment characterization vector for the environment by perturbing the first environment characterization vector; generating a second set of behavioral trajectories associated with one or more potential actions of the entity based on the source content and the second characterization vector for the environment; and training a behavior model for a virtual agent based on the first and second sets of behavioral trajectories in order to imitate the entity.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices:
 obtaining source content that includes an entity performing one or more actions within an environment; 
 generating a first environment characterization vector including a plurality of characterization information portions characterizing the environment; 
 generating a first set of behavioral trajectories associated with the one or more actions of the entity based on the source content and the first characterization vector for the environment; 
 generating a second environment characterization vector for the environment by perturbing at least some of the plurality of characterization information portions; 
 generating a second set of behavioral trajectories associated with one or more potential actions of the entity based on the source content and the second characterization vector for the environment; and 
 training a behavior model for a virtual agent based on the first and second sets of behavioral trajectories in order to imitate the entity. 
 
 
     
     
       2. The method of  claim 1 , wherein the characterization information portions include at least one of information characterizing a first set of objects within the environment or information characterizing a first set of environmental conditions. 
     
     
       3. The method of  claim 2 , wherein perturbing at least some of the plurality of environmental characterization information portions includes adding, removing, or modifying at least some objects within the first set of objects. 
     
     
       4. The method of  claim 2 , wherein perturbing at least some of the plurality of environmental characterization information portions includes modifying at least some environmental conditions associated with the first set of environmental conditions. 
     
     
       5. The method of  claim 1 , wherein each of the first set of behavioral trajectories correspond to physical motion plans. 
     
     
       6. The method of  claim 1 , wherein the source content corresponds to one of a live image stream, a locally captured image stream, a remotely captured image stream, a movie, or a TV episode. 
     
     
       7. The method of  claim 1 , wherein generating the first characterization vector includes performing one of image segmentation or instance segmentation on the source content. 
     
     
       8. The method of  claim 1 , wherein the entity corresponds to a humanoid or an animal. 
     
     
       9. The method of  claim 1 , further comprising:
 generating a three-dimensional (3 D) model associated with the entity based on the source content, wherein the virtual agent corresponds to the  3 D model. 
 
     
     
       10. The method of  claim 1 , further comprising:
 generating an initial behavior model for the entity based on the source content, wherein the first set of behavioral trajectories are generated based on the initial behavior model and the first environment characterization vector, and wherein the second set of behavioral trajectories are generated based on the initial behavior model and the second environment characterization vector. 
 
     
     
       11. The method of  claim 10 , wherein training the behavior model for the virtual agent corresponds to updating the initial behavior model based on the first and second sets of behavioral trajectories. 
     
     
       12. The method of  claim 1 , further comprising:
 presenting, via the display device, the virtual agent performing one or more actions based on the trained behavior model. 
 
     
     
       13. A device comprising:
 one or more processors; 
 a non-transitory memory; 
 an interface for communicating with a display device and one or more input devices; and 
 one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the device to:
 obtain source content that includes an entity performing one or more actions within an environment; 
 generate a first environment characterization vector including a plurality of characterization information portions characterizing the environment; 
 generate a first set of behavioral trajectories associated with the one or more actions of the entity based on the source content and the first characterization vector for the environment; 
 generate a second environment characterization vector for the environment by perturbing at least some of the plurality of characterization information portions; 
 generate a second set of behavioral trajectories associated with one or more potential actions of the entity based on the source content and the second characterization vector for the environment; and 
 train a behavior model for a virtual agent based on the first and second sets of behavioral trajectories in order to imitate the entity. 
 
 
     
     
       14. The device of  claim 13 , wherein the one or more programs further cause the device to:
 generate an initial behavior model for the entity based on the source content, wherein the first set of behavioral trajectories are generated based on the initial behavior model and the first environment characterization vector, and wherein the second set of behavioral trajectories are generated based on the initial behavior model and the second environment characterization vector. 
 
     
     
       15. The device of  claim 14 , wherein training the behavior model for the virtual agent corresponds to updating the initial behavior model based on the first and second sets of behavioral trajectories. 
     
     
       16. The device of  claim 13 , wherein the characterization information portions include at least one of information characterizing a first set of objects within the environment or information characterizing a first set of environmental conditions. 
     
     
       17. The device of  claim 16 , wherein perturbing at least some of the plurality of environmental characterization information portions includes adding, removing, or modifying at least some objects within the first set of objects. 
     
     
       18. The device of  claim 16 , wherein perturbing at least some of the plurality of environmental characterization information portions includes modifying at least some environmental conditions associated with the first set of environmental conditions. 
     
     
       19. A non-transitory memory storing one or more programs, which, when executed by one or more processors of a device with an interface for communicating with a display device and one or more input devices, cause the device to:
 obtain source content that includes an entity performing one or more actions within an environment; 
 generate a first environment characterization vector including a plurality of characterization information portions characterizing the environment; 
 generate a first set of behavioral trajectories associated with the one or more actions of the entity based on the source content and the first characterization vector for the environment; 
 generate a second environment characterization vector for the environment by perturbing at least some of the plurality of characterization information portions; 
 generate a second set of behavioral trajectories associated with one or more potential actions of the entity based on the source content and the second characterization vector for the environment; and 
 train a behavior model for a virtual agent based on the first and second sets of behavioral trajectories in order to imitate the entity. 
 
     
     
       20. The non-transitory memory of  claim 19 , wherein the one or more programs further cause the device to:
 generate an initial behavior model for the entity based on the source content, wherein the first set of behavioral trajectories are generated based on the initial behavior model and the first environment characterization vector, and wherein the second set of behavioral trajectories are generated based on the initial behavior model and the second environment characterization vector. 
 
     
     
       21. The non-transitory memory of  claim 20 , wherein training the behavior model for the virtual agent corresponds to updating the initial behavior model based on the first and second sets of behavioral trajectories. 
     
     
       22. The non-transitory memory of  claim 19 , wherein the characterization information portions include at least one of information characterizing a first set of objects within the environment or information characterizing a first set of environmental conditions. 
     
     
       23. The non-transitory memory of  claim 22 , wherein perturbing at least some of the plurality of environmental characterization information portions includes adding, removing, or modifying at least some objects within the first set of objects. 
     
     
       24. The non-transitory memory of  claim 22 , wherein perturbing at least some of the plurality of environmental characterization information portions includes modifying at least some environmental conditions associated with the first set of environmental conditions.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 63/139,898, filed on Jan. 21, 2021, which is herein incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to training behavior models and, in particular, to systems, devices, and methods for modeling one or more behaviors with synthetic training data. 
     BACKGROUND 
     Imitation learning may be leveraged to train a virtual agent (down to the root motion level) based on real-world behaviors. However, this approach requires a large amount of varied training data with respect to the real-world behaviors in order to produce a well-trained virtual agent. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIG.  1    is a block diagram of an example operating architecture in accordance with some implementations. 
         FIG.  2    is a block diagram of an example controller in accordance with some implementations. 
         FIG.  3    is a block diagram of an example electronic device in accordance with some implementations. 
         FIG.  4 A  is a block diagram of an example training architecture in accordance with some implementations. 
         FIGS.  4 B and  4 C  illustrate example data structures in accordance with some implementations. 
         FIG.  4 D  is a block diagram of an example neural network in accordance with some implementations. 
         FIGS.  5 A and  5 B  illustrate example environments in accordance with some implementations. 
         FIG.  6    is a block diagram of an example runtime architecture in accordance with some implementations. 
         FIG.  7    is a flowchart representation of a method of training a behavior model for a virtual agent based at least in part on synthetic training data in accordance with some implementations. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method, or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods for training a behavior model for a virtual agent based at least in part on synthetic training data. According to some implementations, the method is performed at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices. The method includes: obtaining source content that includes an entity performing one or more actions within an environment; generating a first environment characterization vector including a plurality of characterization information portions characterizing the environment; generating a first set of behavioral trajectories associated with the one or more actions of the entity based on the source content and the first characterization vector for the environment; generating a second environment characterization vector for the environment by perturbing at least some of the plurality of characterization information portions; generating a second set of behavioral trajectories associated with one or more potential actions of the entity based on the source content and the second characterization vector for the environment; and training a behavior model for a virtual agent based on the first and second sets of behavioral trajectories in order to imitate the entity. 
     In accordance with some implementations, an electronic device includes one or more displays, one or more processors, a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions, which, when executed by one or more processors of a device, cause the device to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes: one or more displays, one or more processors, a non-transitory memory, and means for performing or causing performance of any of the methods described herein. 
     In accordance with some implementations, a computing system includes one or more processors, non-transitory memory, an interface for communicating with a display device and one or more input devices, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of the operations of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions which when executed by one or more processors of a computing system with an interface for communicating with a display device and one or more input devices, cause the computing system to perform or cause performance of the operations of any of the methods described herein. In accordance with some implementations, a computing system includes one or more processors, non-transitory memory, an interface for communicating with a display device and one or more input devices, and means for performing or causing performance of the operations of any of the methods described herein. 
     DESCRIPTION 
     Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects and/or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices, and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein. 
     A physical environment refers to a physical world that people can sense and/or interact with without aid of electronic devices. The physical environment may include physical features such as a physical surface or a physical object. For example, the physical environment corresponds to a physical park that includes physical trees, physical buildings, and physical people. People can directly sense and/or interact with the physical environment such as through sight, touch, hearing, taste, and smell. In contrast, an extended reality (XR) environment refers to a wholly or partially simulated environment that people sense and/or interact with via an electronic device. For example, the XR environment may include augmented reality (AR) content, mixed reality (MR) content, virtual reality (VR) content, and/or the like. With an XR system, a subset of a person&#39;s physical motions, or representations thereof, are tracked, and, in response, one or more characteristics of one or more virtual objects simulated in the XR environment are adjusted in a manner that comports with at least one law of physics. As one example, the XR system may detect head movement and, in response, adjust graphical content and an acoustic field presented to the person in a manner similar to how such views and sounds would change in a physical environment. As another example, the XR system may detect movement of the electronic device presenting the XR environment (e.g., a mobile phone, a tablet, a laptop, or the like) and, in response, adjust graphical content and an acoustic field presented to the person in a manner similar to how such views and sounds would change in a physical environment. In some situations (e.g., for accessibility reasons), the XR system may adjust characteristic(s) of graphical content in the XR environment in response to representations of physical motions (e.g., vocal commands). 
     There are many different types of electronic systems that enable a person to sense and/or interact with various XR environments. Examples include head mountable systems, projection-based systems, heads-up displays (HUDs), vehicle windshields having integrated display capability, windows having integrated display capability, displays formed as lenses designed to be placed on a person&#39;s eyes (e.g., similar to contact lenses), headphones/earphones, speaker arrays, input systems (e.g., wearable or handheld controllers with or without haptic feedback), smartphones, tablets, and desktop/laptop computers. A head mountable system may have one or more speaker(s) and an integrated opaque display. Alternatively, ahead mountable system may be configured to accept an external opaque display (e.g., a smartphone). The head mountable system may incorporate one or more imaging sensors to capture images or video of the physical environment, and/or one or more microphones to capture audio of the physical environment. Rather than an opaque display, a head mountable system may have a transparent or translucent display. The transparent or translucent display may have a medium through which light representative of images is directed to a person&#39;s eyes. The display may utilize digital light projection, OLEDs, LEDs, uLEDs, liquid crystal on silicon, laser scanning light source, or any combination of these technologies. The medium may be an optical waveguide, a hologram medium, an optical combiner, an optical reflector, or any combination thereof. In some implementations, the transparent or translucent display may be configured to become opaque selectively. Projection-based systems may employ retinal projection technology that projects graphical images onto a person&#39;s retina. Projection systems also may be configured to project virtual objects into the physical environment, for example, as a hologram or on a physical surface. 
       FIG.  1    is a block diagram of an example operating architecture  100  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating architecture  100  includes an optional controller  110  and an electronic device  120  (e.g., a tablet, mobile phone, laptop, near-eye system, wearable computing device, or the like). 
     In some implementations, the controller  110  is configured to manage and coordinate an XR experience (sometimes also referred to herein as a “XR environment” or a “virtual environment” or a “graphical environment”) for a user  150  and optionally other users. In some implementations, the controller  110  includes a suitable combination of software, firmware, and/or hardware. The controller  110  is described in greater detail below with respect to  FIG.  2   . In some implementations, the controller  110  is a computing device that is local or remote relative to the physical environment  105 . For example, the controller  110  is a local server located within the physical environment  105 . In another example, the controller  110  is a remote server located outside of the physical environment  105  (e.g., a cloud server, central server, etc.). In some implementations, the controller  110  is communicatively coupled with the electronic device  120  via one or more wired or wireless communication channels  144  (e.g., BLUETOOTH, IEEE 802.11 x, IEEE 802.16 x, IEEE 802.3 x, etc.). In some implementations, the functions of the controller  110  are provided by the electronic device  120 . As such, in some implementations, the components of the controller  110  are integrated into the electronic device  120 . 
     In some implementations, the electronic device  120  is configured to present audio and/or video (A/V) content to the user  150 . In some implementations, the electronic device  120  is configured to present a user interface (UI) and/or an XR environment  128  to the user  150 . In some implementations, the electronic device  120  includes a suitable combination of software, firmware, and/or hardware. The electronic device  120  is described in greater detail below with respect to  FIG.  3   . 
     According to some implementations, the electronic device  120  presents an XR experience to the user  150  while the user  150  is physically present within a physical environment  105  that includes a table  107  within the field-of-view (FOV)  111  of the electronic device  120 . As such, in some implementations, the user  150  holds the electronic device  120  in his/her hand(s). In some implementations, while presenting the XR experience, the electronic device  120  is configured to present XR content (sometimes also referred to herein as “graphical content” or “virtual content”), including an XR cylinder  109 , and to enable video pass-through of the physical environment  105  (e.g., including the table  107 ) on a display  122 . For example, the XR environment  128 , including the XR cylinder  109 , is volumetric or three-dimensional (3 D). 
     In one example, the XR cylinder  109  corresponds to display-locked content such that the XR cylinder  109  remains displayed at the same location on the display  122  as the FOV  111  changes due to translational and/or rotational movement of the electronic device  120 . As another example, the XR cylinder  109  corresponds to world-locked content such that the XR cylinder  109  remains displayed at its origin location as the FOV  111  changes due to translational and/or rotational movement of the electronic device  120 . As such, in this example, if the FOV  111  does not include the origin location, the XR environment  128  will not include the XR cylinder  109 . For example, the electronic device  120  corresponds to a near-eye system, mobile phone, tablet, laptop, wearable computing device, or the like. 
     In some implementations, the display  122  corresponds to an additive display that enables optical see-through of the physical environment  105  including the table  107 . For example, the display  122  correspond to a transparent lens, and the electronic device  120  corresponds to a pair of glasses worn by the user  150 . As such, in some implementations, the electronic device  120  presents a user interface by projecting the XR content (e.g., the XR cylinder  109 ) onto the additive display, which is, in turn, overlaid on the physical environment  105  from the perspective of the user  150 . In some implementations, the electronic device  120  presents the user interface by displaying the XR content (e.g., the XR cylinder  109 ) on the additive display, which is, in turn, overlaid on the physical environment  105  from the perspective of the user  150 . 
     In some implementations, the user  150  wears the electronic device  120  such as a near-eye system. As such, the electronic device  120  includes one or more displays provided to display the XR content (e.g., a single display or one for each eye). For example, the electronic device  120  encloses the FOV of the user  150 . In such implementations, the electronic device  120  presents the XR environment  128  by displaying data corresponding to the XR environment  128  on the one or more displays or by projecting data corresponding to the XR environment  128  onto the retinas of the user  150 . 
     In some implementations, the electronic device  120  includes an integrated display (e.g., a built-in display) that displays the XR environment  128 . In some implementations, the electronic device  120  includes a head-mountable enclosure. In various implementations, the head-mountable enclosure includes an attachment region to which another device with a display can be attached. For example, in some implementations, the electronic device  120  can be attached to the head-mountable enclosure. In various implementations, the head-mountable enclosure is shaped to form a receptacle for receiving another device that includes a display (e.g., the electronic device  120 ). For example, in some implementations, the electronic device  120  slides/snaps into or otherwise attaches to the head-mountable enclosure. In some implementations, the display of the device attached to the head-mountable enclosure presents (e.g., displays) the XR environment  128 . In some implementations, the electronic device  120  is replaced with an XR chamber, enclosure, or room configured to present XR content in which the user  150  does not wear the electronic device  120 . 
     In some implementations, the controller  110  and/or the electronic device  120  cause an XR representation of the user  150  to move within the XR environment  128  based on movement information (e.g., body pose data, eye tracking data, hand/limb/finger/extremity tracking data, etc.) from the electronic device  120  and/or optional remote input devices within the physical environment  105 . In some implementations, the optional remote input devices correspond to fixed or movable sensory equipment within the physical environment  105  (e.g., image sensors, depth sensors, infrared (IR) sensors, event cameras, microphones, etc.). In some implementations, each of the remote input devices is configured to collect/capture input data and provide the input data to the controller  110  and/or the electronic device  120  while the user  150  is physically within the physical environment  105 . In some implementations, the remote input devices include microphones, and the input data includes audio data associated with the user  150  (e.g., speech samples). In some implementations, the remote input devices include image sensors (e.g., cameras), and the input data includes images of the user  150 . In some implementations, the input data characterizes body poses of the user  150  at different times. In some implementations, the input data characterizes head poses of the user  150  at different times. In some implementations, the input data characterizes hand tracking information associated with the hands of the user  150  at different times. In some implementations, the input data characterizes the velocity and/or acceleration of body parts of the user  150  such as his/her hands. In some implementations, the input data indicates joint positions and/or joint orientations of the user  150 . In some implementations, the remote input devices include feedback devices such as speakers, lights, or the like. 
       FIG.  2    is a block diagram of an example of the controller  110  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the controller  110  includes one or more processing units  202  (e.g., microprocessors, application-specific integrated-circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), central processing units (CPUs), processing cores, and/or the like), one or more input/output (I/O) devices  206 , one or more communication interfaces  208  (e.g., universal serial bus (USB), IEEE 802.3 x, IEEE 802.11 x, IEEE 802.16 x, global system for mobile communications (GSM), code division multiple access (CDMA), time division multiple access (TDMA), global positioning system (GPS), infrared (IR), BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  210 , a memory  220 , and one or more communication buses  204  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  204  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  206  include at least one of a keyboard, a mouse, a touchpad, a touch-screen, a joystick, one or more microphones, one or more speakers, one or more image sensors, one or more displays, and/or the like. 
     The memory  220  includes high-speed random-access memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), double-data-rate random-access memory (DDR RAM), or other random-access solid-state memory devices. In some implementations, the memory  220  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  220  optionally includes one or more storage devices remotely located from the one or more processing units  202 . The memory  220  comprises a non-transitory computer readable storage medium. In some implementations, the memory  220  or the non-transitory computer readable storage medium of the memory  220  stores the following programs, modules and data structures, or a subset thereof described below with respect to  FIG.  2   . 
     The operating system  230  includes procedures for handling various basic system services and for performing hardware dependent tasks. 
     In some implementations, a data obtainer  242  is configured to obtain data (e.g., captured image frames of the physical environment  105 , presentation data, input data, user interaction data, camera pose tracking information, eye tracking information, head/body pose tracking information, hand/limb/finger/extremity tracking information, sensor data, location data, etc.) from at least one of the I/O devices  206  of the controller  110 , the I/O devices and sensors  306  of the electronic device  120 , and the optional remote input devices. To that end, in various implementations, the data obtainer  242  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a mapper and locator engine  244  is configured to map the physical environment  105  and to track the position/location of at least the electronic device  120  or the user  150  with respect to the physical environment  105 . To that end, in various implementations, the mapper and locator engine  244  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a data transmitter  246  is configured to transmit data (e.g., presentation data such as rendered image frames associated with the XR environment, location data, etc.) to at least the electronic device  120  and optionally one or more other devices. To that end, in various implementations, the data transmitter  246  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a training architecture  400  is configured to populate a virtual agent (VA) library  430  based at least in part on source content  415 . The training architecture  400  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the training architecture  400  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the training architecture  400  includes: a content analyzer  420 ; a perturbation engine  434 ; a plurality of trajectory generators  440 A,  440 B, . . . ; an ML system  450 ; a reward estimator  452 ; and an adjustor  454 . 
     In some implementations, the content analyzer  420  is configured to ingest and analyze the source content  415  that includes an entity performing one or more actions within an environment. For example, the source content  415  corresponds to one of: a live image/video stream such as a locally or remotely captured image/video stream; pre-existing audio/video (A/V) such as a movie, a TV episode, etc.; or the like. In some implementations, the content analyzer  420  is also configured to identify or select the entity within the source content  415  (e.g., based on a user input, object recognition, and/or the like). In some implementations, the content analyzer  420  is further configured to generate, based on the source content  415 , a three-dimensional (3 D) model  422 A and an initial behavior model  424 A for the entity. In some implementations, the content analyzer  420  is further configured to generate, based on the source content  415 , a first environment characterization vector  433  for the environment including a plurality of information portions characterizing the environment. Environment characterization vectors are described in more detail below with reference to  FIG.  4 B . The content analyzer  420  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the content analyzer  420  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the perturbation engine  434  is configured to generate a second environment characterization vector  435  for the environment by perturbing at least some of the plurality of characterization information portions of the first environment characterization vector  433 . The perturbation engine  434  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the perturbation engine  434  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the trajectory generator  440 A is configured to generate a first set of behavioral trajectories  442  associated with the one or more actions of the entity based on the initial behavior model  424 A and the first characterization vector for the environment  433 . In some implementations, the trajectory generator  440 B is configured to generate a second set of behavioral trajectories  444  associated with one or more potential actions of the entity based on the initial behavior model  424 A and the second characterization vector for the environment  435 . The plurality of trajectory generators  440 A,  440 B, . . . are described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the plurality of trajectory generators  440 A,  440 B, . . . include instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the machine learning (ML) system  450  is configured to generate a trained behavior model  451 A based on the first set of behavioral trajectories  442  and the second set of behavioral trajectories  444 . For example, the ML system  450  corresponds to a neural network (NN), a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a support vector machine (SVM), a relevance vector machine (RVM), a random forest algorithm, or the like. The ML system  450  is described in more detail below with reference to  FIG.  4 D . To that end, in various implementations, the ML system  450  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the reward estimator  452  is configured to generate a reward signal  453  (e.g., a confidence score, a quality score, or the like) for the trained behavior model  451 A and to send the reward signal  453  to the adjustor  454 . The reward estimator  452  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the reward estimator  452  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the adjustor  454  is configured to adjust one or more operating parameters  455  (e.g., filter weights, neurons, etc.) of the ML system  450  in accordance with a determination that the reward signal  453  fails to satisfy a threshold convergence metric. In some implementations, the adjustor  454  is configured to forgo adjusting the one or more operating parameters  455  of the ML system  450  in accordance with a determination that the reward signal  453  satisfies the threshold convergence metric. The adjustor  454  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the adjustor  454  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the VA library  430  stores a plurality of entries (e.g., rows) each associated with a respective subject (or virtual agent) such as the entity within the environment from the source content  415 . According to some implementations, a respective entry within the VA library  430  that corresponds to the entity within the environment from the source content  415  includes an identifier  421 A associated with the entity such as a label or the like, the 3 D model  422 A, the initial behavior model  424 A, the trained behavior model  451 A (that satisfies the threshold convergence metric), and/or miscellaneous information  428 A. The VA library  430  is described in more detail below with reference to  FIG.  4 C . In some implementations, the VA library  430  is stored locally relative to the controller  110 . In some implementations, the VA library  430  is located remote from the controller  110  (e.g., at a remote server, a cloud server, or the like). 
     In some implementations, a runtime architecture  600  is configured to animate a VA within an XR environment based on the VA library  430  populated by the training architecture  400 . The runtime architecture  600  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the runtime architecture  600  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the runtime architecture  600  includes: a content selector  612 , an animation engine  620 , and a rendering engine  650 . 
     In some implementations, the content selector  612  is configured to select a VA from the VA library  430  (and its associated 3 D model and trained behavior model) based on one or more user requests and/or inputs (e.g., a voice command, a selection from a user interface (UI) menu of VAs, and/or the like). The content selector  612  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the content selector  612  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, an animation engine  620  is configured to generate an animation of the selected VA performing one or more actions based on the 3 D model  431 A associated with the selected VA and the trained behavior model  451 A associated with the selected VA. The animation engine  620  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the animation engine  620  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a rendering engine  650  is configured to render a XR environment (sometimes also referred to herein as a “graphical environment” or “virtual environment”) or image frame associated therewith that includes the animation of the selected VA. To that end, in various implementations, the rendering engine  650  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the rendering engine  650  includes a pose determiner  652 , a renderer  654 , an optional image processing architecture  662 , and an optional compositor  664 . 
     In some implementations, the pose determiner  652  is configured to determine a current camera pose of the electronic device  120  and/or the user  150  relative to the selected VA and/or other XR content associated with the XR environment. The pose determiner  652  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the pose determiner  652  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the renderer  654  is configured to render the XR environment including the animation of the selected VA according to the current camera pose relative thereto. The renderer  654  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the renderer  654  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the image processing architecture  662  is configured to obtain (e.g., receive, retrieve, or capture) an image stream including one or more images of the physical environment  105  from the current camera pose of the electronic device  120  and/or the user  150 . In some implementations, the image processing architecture  662  is also configured to perform one or more image processing operations on the image stream such as warping, color correction, gamma correction, sharpening, noise reduction, white balance, and/or the like. The image processing architecture  662  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the image processing architecture  662  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the compositor  664  is configured to composite the rendered XR environment with the processed image stream of the physical environment  105  from the image processing architecture  662  to produce rendered image frames of the XR environment for display. The compositor  664  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the compositor  664  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtainer  242 , the mapper and locator engine  244 , the data transmitter  246 , the training architecture  400 , and the runtime architecture  600  are shown as residing on a single device (e.g., the controller  110 ), it should be understood that in other implementations, any combination of the data obtainer  242 , the mapper and locator engine  244 , the data transmitter  246 , the training architecture  400 , and the runtime architecture  600  may be located in separate computing devices. 
     In some implementations, the functions and/or components of the controller  110  are combined with or provided by the electronic device  120  shown below in  FIG.  3   . Moreover,  FIG.  2    is intended more as a functional description of the various features which be present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  2    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation. 
       FIG.  3    is a block diagram of an example of the electronic device  120  (e.g., a mobile phone, tablet, laptop, near-eye system, wearable computing device, or the like) in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the electronic device  120  includes one or more processing units  302  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  306 , one or more communication interfaces  308  (e.g., USB, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  310 , one or more displays  312 , an image capture device  370  (e.g., one or more optional interior- and/or exterior-facing image sensors), a memory  320 , and one or more communication buses  304  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  304  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  306  include at least one of an inertial measurement unit (IMU), an accelerometer, a gyroscope, a magnetometer, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oximetry monitor, blood glucose monitor, etc.), one or more microphones, one or more speakers, a haptics engine, a heating and/or cooling unit, a skin shear engine, one or more depth sensors (e.g., structured light, time-of-flight, LiDAR, or the like), a localization and mapping engine, an eye tracking engine, a body/head pose tracking engine, a hand/limb/finger/extremity tracking engine, a camera pose tracking engine, or the like. 
     In some implementations, the one or more displays  312  are configured to present the XR environment to the user. In some implementations, the one or more displays  312  are also configured to present flat video content to the user (e.g., a 2-dimensional or “flat” AVI, FLV, WMV, MOV, MP4, or the like file associated with a TV episode or a movie, or live video pass-through of the physical environment  105 ). In some implementations, the one or more displays  312  correspond to touchscreen displays. In some implementations, the one or more displays  312  correspond to holographic, digital light processing (DLP), liquid-crystal display (LCD), liquid-crystal on silicon (LCoS), organic light-emitting field-effect transitory (OLET), organic light-emitting diode (OLED), surface-conduction electron-emitter display (SED), field-emission display (FED), quantum-dot light-emitting diode (QD-LED), micro-electro-mechanical system (MEMS), and/or the like display types. In some implementations, the one or more displays  312  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the electronic device  120  includes a single display. In another example, the electronic device  120  includes a display for each eye of the user. In some implementations, the one or more displays  312  are capable of presenting AR and VR content. In some implementations, the one or more displays  312  are capable of presenting AR or VR content. 
     In some implementations, the image capture device  370  correspond to one or more RGB cameras (e.g., with a complementary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), IR image sensors, event-based cameras, and/or the like. In some implementations, the image capture device  370  includes a lens assembly, a photodiode, and a front-end architecture. In some implementations, the image capture device  370  includes exterior-facing and/or interior-facing image sensors. 
     The memory  320  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  320  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  320  optionally includes one or more storage devices remotely located from the one or more processing units  302 . The memory  320  comprises a non-transitory computer readable storage medium. In some implementations, the memory  320  or the non-transitory computer readable storage medium of the memory  320  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  330  and a presentation engine  340 . 
     The operating system  330  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the presentation engine  340  is configured to present media items and/or XR content to the user via the one or more displays  312 . To that end, in various implementations, the presentation engine  340  includes a data obtainer  342 , a presenter  670 , an interaction handler  610 , and a data transmitter  350 . 
     In some implementations, the data obtainer  342  is configured to obtain data (e.g., presentation data such as rendered image frames associated with the user interface or the XR environment, blended animation(s), input data, user interaction data, head tracking information, camera pose tracking information, eye tracking information, hand/limb/finger/extremity tracking information, sensor data, location data, etc.) from at least one of the I/O devices and sensors  306  of the electronic device  120 , the controller  110 , and the remote input devices. To that end, in various implementations, the data obtainer  342  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the presenter  670  is configured to present and update A/V content and/or XR content (e.g., the rendered image frames associated with the user interface or the XR environment) via the one or more displays  312 . To that end, in various implementations, the presenter  670  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the interaction handler  610  is configured to detect user interactions with the presented A/V content and/or XR content (e.g., gestural inputs detected via hand tracking, eye gaze inputs detected via eye tracking, voice commands, etc.). To that end, in various implementations, the interaction handler  610  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitter  350  is configured to transmit data (e.g., presentation data, location data, user interaction data, head tracking information, camera pose tracking information, eye tracking information, hand/limb/finger/extremity tracking information, etc.) to at least the controller  110 . To that end, in various implementations, the data transmitter  350  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtainer  342 , the interaction handler  610 , the presenter  670 , and the data transmitter  350  are shown as residing on a single device (e.g., the electronic device  120 ), it should be understood that in other implementations, any combination of the data obtainer  342 , the interaction handler  610 , the presenter  670 , and the data transmitter  350  may be located in separate computing devices. 
     Moreover,  FIG.  3    is intended more as a functional description of the various features which be present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  3    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation. 
       FIG.  4 A  is a block diagram of an example training architecture  400  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the animation architecture  400  is included in a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof 
     As shown in  FIG.  4 A , the content analyzer  420  ingests the source content  415  that includes an entity performing one or more actions within an environment. For example, the source content  415  corresponds to one of: a live image/video stream such as a locally or remotely captured image/video stream; pre-existing audio/video (A/V) such as a movie, a TV episode, etc.; or the like. 
     According to some implementations, the content analyzer  420  identifies or selects an entity within the source content  415  (e.g., based on a user input, object recognition, instance segmentation, and/or the like). As shown in  FIG.  4 A , the content analyzer  420  generates, based on the source content  415 , a 3 D model  422 A for the entity (e.g., based on photogrammetry or other computer vision techniques) and an initial behavior model  424 A for the entity (e.g., based on behavior recognition techniques). As shown in  FIG.  4 A , the training architecture  400  stores the 3 D model  422 A and the initial behavior model  424 A in an entry associated with the entity within the VA library  430 . In some implementations, the training architecture  400  determines an object label for the entity and obtains (e.g., receives or retrieves) a 3 D model for the entity and a generic behavior model for the entity from a model library based on the object label. As such, for example, if the training architecture  400  determines that the entity corresponds to a Labrador Retriever, the training architecture  400  obtains a 3 D model associated with Labrador Retrievers and a generic behavior model associated with Labrador Retrievers from the model library. 
     As shown in  FIG.  4 A , the content analyzer  420  generates, based on the source content  415 , a first environment characterization vector  433  for the environment including a plurality of information portions characterizing the environment (e.g., based on image segmentation techniques, instance segmentation techniques, object recognition techniques, 3 D scene reconstruction techniques, etc.).  FIG.  4 B  shows an example data structure for the first environment characterization vector  433  in accordance with some implementations. One of ordinary skill in the art will appreciate that the first environment characterization vector  433  shown in  FIG.  4 B  is an example data structure that may be formatted differently and otherwise modified in various other implementations. According to some implementations, the first environment characterization vector  433  characterizes an initial state of the environment, where the environment corresponds to a theatrical scene or the like within the source content  415 . As shown in  FIG.  4 B , the first environment characterization vector  433  includes: a spatial representation of the environment  461  (e.g., a point cloud, 3 D model, and/or the like); one or more object representations  462  (e.g., 3 D models) associated with entities, objects, content, etc. recognized within the environment; one or more object labels  463  associated with the entities, objects, obstacles, content, etc. recognized within the initial state of the environment; environmental conditions  464  associated with the initial state of the environment (e.g., ambient audio information, ambient lighting information, weather parameters, etc.); and miscellaneous information  465  associated with the initial state of the environment. 
     As shown in  FIG.  4 A , the perturbation engine  434  generates a second environment characterization vector  435  for the environment by perturbing (or modifying) at least some of the plurality of characterization information portions of the first environment characterization vector  433 . In some implementations, the perturbation engine  434  perturbs (or modifies) at least some of the plurality of characterization information portions of the first environment characterization vector  433  based on a random, pseudo-random, deterministic, procedural, and/or a like process. 
       FIG.  4 B  shows an example data structure for the second environment characterization vector  435  in accordance with some implementations. One of ordinary skill in the art will appreciate that the second environment characterization vector  435  shown in  FIG.  4 B  is an example data structure that may be formatted differently and otherwise modified in various other implementations. According to some implementations, the second environment characterization vector  435  environment characterizes a modified state of the environment. As shown in  FIG.  4 B , the second environment characterization vector  435  includes: a modified spatial representation of the environment  471  (e.g., a modified point cloud, 3 D model, and/or the like); one or more modified object representations  472  (e.g., 3 D models) associated with modified entities, objects, content, etc. within the modified state of the environment; one or more object labels  473  associated with the modified entities, objects, obstacles, content, etc. within the modified state of the environment; modified environmental conditions  474  associated with the modified state of the environment (e.g., modified ambient audio information, ambient lighting information, weather parameters, etc.); and miscellaneous information  475  associated with the modified state of the environment. 
     As shown in  FIG.  4 A , the trajectory generator  440 A generates a first set of behavioral trajectories  442  associated with the one or more actions of the entity with the source content  415  based on the initial behavior model  424 A and the first characterization vector for the environment  433 . As shown in  FIG.  4 A , the trajectory generator  440 B generates a second set of behavioral trajectories  444  associated with one or more potential actions of the entity based on the initial behavior model  424 A and the second characterization vector for the environment  435 . One of ordinary skill in the art will appreciate that the plurality of trajectory generators  440 A,  440 B, ... may correspond to a single trajectory generator in various implementations that generates the first set of behavioral trajectories  442  and the second set of behavioral trajectories  444  in series or in parallel. One of ordinary skill in the art will appreciate that although the training architecture  400  in  FIG.  4 A  illustrates two trajectory generators  440 A and  440 B, the training architecture  400  may include additional trajectory generators that generate additional sets of behavioral trajectories based on additional environment characterization vectors from the perturbation engine  434 . 
     As shown in  FIG.  4 A , the ML system  450  generate a trained behavior model  451 A based on the first set of behavioral trajectories  442  and the second set of behavioral trajectories  444 . In some implementations, the ML system  450  corresponds to an NN, a CNN, a DNN, a RNN, an SVM, an RVM, a random forest algorithm, or the like. 
     As shown in  FIG.  4 A , the reward estimator  452  generates a reward signal  453  (e.g., a confidence or quality score) for the trained behavior model  451 A and sends the reward signal  453  to the adjustor  454 . As shown in  FIG.  4 A , the adjustor  454  adjusts one or more operating parameters  455  (e.g., filter weights, neurons, etc.) of the ML system  450  in accordance with a determination that the reward signal  453  fails to satisfy a threshold convergence metric. As shown in  FIG.  4 A , the adjustor  454  forgoes adjusting the one or more operating parameters  455  of the ML system  450  in accordance with a determination that the reward signal  453  satisfies the threshold convergence metric. As shown in  FIG.  4 A , the training architecture  400  stores the trained behavior model  451 A in the entry associated with the entity within the VA library  430  when the reward signal  453  satisfies the threshold convergence metric. 
       FIG.  4 C  shows an example data structure for the VA library  430  in accordance with some implementations. One of ordinary skill in the art will appreciate that the VA library  430  shown in  FIG.  4 C  is an example data structure that may be formatted differently and otherwise modified in various other implementations. As shown in  FIG.  4 C , the VA library  430  includes a plurality of different entries  425 A,  425 B, . . . ,  425 N each associated with a different entity (or VA). For example, the entry  425 A associated with the respective entity referenced with respect to  FIG.  4 A  includes: an identifier  421 A associated with the respective entity such as a label, a serial number, or the like; the 3 D model  422 A for the respective entity; 
     the initial behavior model  424 A associated with the respective entity; the trained behavior model  451 A associated with the respective entity (that satisfies the threshold convergence metric); and/or miscellaneous information  428 A associated with the respective entity. The entries  425 B, . . . ,  425 N includes similar information portions but are not described for the sake of brevity. 
       FIG.  4 D  is a block diagram of an example neural network  490  according to some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. According to some implementations, the neural network  490  may correspond to the ML system  450  in  FIGS.  2  and  4 A . To that end, as a non-limiting example, in some implementations, the neural network  490  includes an input layer  492 , a first hidden layer  494 , a second hidden layer  496 , and an output layer  498 . While the neural network  490  includes two hidden layers as an example, those of ordinary skill in the art will appreciate from the present disclosure that one or more additional hidden layers are also present in various implementations. Adding additional hidden layers adds to the computational complexity and memory demands but may improve performance for some applications. 
     In various implementations, the input layer  492  is coupled (e.g., configured) to receive an input  4100 . For example, with reference to  FIG.  4 A , the input  4100  corresponds to the first set of behavioral trajectories  442  and the second set of behavioral trajectories  444 . In various implementations, the input layer  492  includes a number of long short-term memory (LSTM) logic units  493  or the like, which are also referred to as model(s) of neurons by those of ordinary skill in the art. In some such implementations, an input matrix from the features to the LSTM logic units  493  include rectangular matrices. For example, the size of this matrix is a function of the number of features included in the feature stream. 
     In some implementations, the first hidden layer  494  includes a number of LSTM logic units  495  or the like. As illustrated in the example of  FIG.  4 D , the first hidden layer  494  receives its inputs from the input layer  492 . For example, the first hidden layer  494  performs one or more of following: a convolutional operation, a nonlinearity operation, a normalization operation, a pooling operation, and/or the like. In some implementations, the number of LSTM logic units  495  is the same as or similar to the number of LSTM logic units  493  in the input layer  492 . 
     In some implementations, the second hidden layer  496  includes a number of LSTM logic units  497  or the like. In some implementations, the number of LSTM logic units  497  is the same as or similar to the number of LSTM logic units  493  in the input layer  492  or the number of LSTM logic units  495  in the first hidden layer  494 . As illustrated in the example of  FIG.  4 D , the second hidden layer  496  receives its inputs from the first hidden layer  494 . Additionally, and/or alternatively, in some implementations, the second hidden layer  496  receives its inputs from the input layer  492 . For example, the second hidden layer  496  performs one or more of following: a convolutional operation, a nonlinearity operation, a normalization operation, a pooling operation, and/or the like. 
     In some implementations, the output layer  498  includes a number of LSTM logic units  499  or the like. In some implementations, the number of LSTM logic units  499  is the same as or similar to the number of LSTM logic units  493  in the input layer  492 , the number of LSTM logic units  495  in the first hidden layer  494 , or the number of LSTM logic units  497  in the second hidden layer  496 . In some implementations, the output layer  498  is a task-dependent layer that performs behavioral trajectory tasks, behavior modeling tasks, or other related tasks. In some implementations, the output layer  498  includes an implementation of a multinomial logistic function (e.g., a soft-max function) that produces an output  4102 . For example, with reference to  FIG.  4 A , the output  4102  corresponds to the trained behavior model  451 A. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
       FIG.  5 A  illustrates an initial state of an environment  510 A and a modified state of the environment  510 B in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example,  FIG.  5 A  illustrates an initial state of the environment  510 A and a modified state of the environment  510 B. For example, the initial state of the environment  510 A corresponds to an image or portion of the source content  415 , and the modified state of the environment  510 B corresponds to a perturbed or artificially modified version of the initial state of the environment  510 A. 
     As shown in  FIG.  5 A , the initial state of the environment  510 A includes an entity  522 A (e.g., a bird) and a date palm  524 A from which the entity  522 A is attempting to gather food (e.g., dates). In the initial state of the environment  510 A, the date palm  524 A includes five fronds. As shown in  FIG.  5 A , the modified state of the environment  510 B includes a representation of the entity  522 B (e.g., a 3 D model of the bird) and a modified representation of the date palm  524 B from which the representation of the entity  522 B is attempting to gather food (e.g., dates). In the modified state of the environment  510 B, the modified representation of the date palm  524 B includes more fronds—ten in total as compared to five fronds. As such, in this example, the representation of the entity  522 B modifies its trajectory or path to gather food (e.g., dates) from the modified representation of the date palm  524 B. 
       FIG.  5 B  illustrates an initial state of an environment  540 A and a modified state of the environment  540 B in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example,  FIG.  5 B  illustrates an initial state of the environment  540 A and a modified state of the environment  540 B. For example, the initial state of the environment  540 A corresponds to an image or portion of the source content  415 , and the modified state of the environment  540 B corresponds to a perturbed or artificially modified version of the initial state of the environment  540 A. 
     As shown in  FIG.  5 B , the initial state of the environment  540 A includes an entity  552 A (e.g., a humanoid) and an object  556  behind a wall  554 A that the entity  552 A is attempting to locomote to and/or pick up. In the initial state of the environment  540 A, the wall  554 A extends along a single y-axis. As shown in  FIG.  5 B , the modified state of the environment  540 B includes a representation of the entity  552 B (e.g., a 3 D model of the humanoid) and a modified representation of the wall  554 B around which the representation of the entity  552 B is attempting to locomote to and/or pick up the object  556 . In the modified state of the environment  540 B, the modified representation of the wall  554 B includes two additional parallel wings extending along an x-axis. As such, in this example, the representation of the entity  552 B modifies its trajectory or path when locomoting around modified representation of the wall  554 B to pick up the object  556 . 
       FIG.  6    is a block diagram of an example runtime architecture  600  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the content runtime architecture  600  is included in a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof. 
     As shown in  FIG.  6   , the interaction handler  610  obtains (e.g., receives, retrieves, or detects) one or more user inputs  601  provided by the user  150  that are associated with selecting a virtual agent (VA). For example, the one or more user inputs  601  correspond to a gestural input selecting the VA from a UI menu detected via hand tracking, an eye gaze input selecting the VA from the UI menu detected via eye tracking, a voice command selecting the VA from the UI menu detected via a microphone, and/or the like. In some implementations, the content selector  612  obtains the 3 D model  431 A and the trained behavior model  451 A from the VA library  430  that correspond to the selected VA. For example, the VA corresponds to an animal, a vehicle, a robot, a humanoid, or the like, which is instantiated within the XR environment. Continuing with this example, the user  150  may interact with the VA, and the VA may be animatable or otherwise enabled to translate and/or rotate within the XR environment. In various implementations, the animation engine  620  generates an animation  621  including the VA performing one or more actions based on the 3 D model  431 A and the trained behavior model  451 A. 
     According to some implementations, the pose determiner  652  determines a current camera pose of the electronic device  120  and/or the user  150  relative to the VA and/or the physical environment  105 . In some implementations, the renderer  654  renders the XR environment including the animation of the VA according to the current camera pose relative thereto. According to some implementations, the optional image processing architecture  662  obtains an image stream from an image capture device  370  including one or more images of the physical environment  105  from the current camera pose of the electronic device  120  and/or the user  150 . In some implementations, the image processing architecture  662  also performs one or more image processing operations on the image stream such as warping, color correction, gamma correction, sharpening, noise reduction, white balance, and/or the like. In some implementations, the optional compositor  664  composites the rendered XR environment VA with the processed image stream of the physical environment  105  from the image processing architecture  662  to produce rendered image frames of the XR environment. In various implementations, the presenter  670  presents the rendered image frames of the XR environment to the user  150  (e.g., via the one or more displays  312  of the electronic device  120 ). One of ordinary skill in the art will appreciate that the optional image processing architecture  662  and the optional compositor  664  may not be applicable for fully virtual environments (or optical see-through scenarios). 
       FIG.  7    is a flowchart representation of a method  700  of training a behavior model with synthetic training data in accordance with some implementations. In various implementations, the method  700  is performed at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices (e.g., the electronic device  120  shown in  FIGS.  1  and  3   ; the controller  110  in  FIGS.  1  and  2   ; or a suitable combination thereof). In some implementations, the computing system includes the animation architecture  400  in  FIG.  4 A  and the rendering architecture  600  in  FIG.  6   . In some implementations, the method  700  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  700  is performed by a processor executing code stored in anon-transitory computer-readable medium (e.g., a memory). In some implementations, the computing system corresponds to one of a tablet, a laptop, a mobile phone, a near-eye system, a wearable computing device, or the like. In some implementations, the computing system (or a component thereof) corresponds to a virtual agent operating system. 
     As discussed above, imitation learning may be leveraged to train a virtual agent (down to the root motion level) based on real-world behaviors. However, this approach requires a large amount of varied training data with respect to the real-world behaviors in order to produce a well-trained virtual agent. In contrast, according to various implementations, the method described herein increase the entropy of a training corpus by: (A) obtaining primary training data; and (B) perturbing the primary training data to produce secondary training data. The secondary training data may include modified objects, obstacles, environmental conditions, etc. as compared to the primary training data. As such, a virtual agent may be trained on a wider breadth of training data with greater entropy/variability than training on the primary training data alone. 
     As represented by block  7 - 1 , the method  700  includes obtaining source content that includes an entity performing one or more actions within an environment. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the content analyzer  420 ) ingests and analyzes the source content  415  that includes the entity performing one or more actions within the environment. In some implementations, the content analyzer  420  identifies or selects the entity from among a plurality of objects or content within the environment based on a user input, object recognition, and/or the like. 
     In some implementations, the entity corresponds to a human, a humanoid, an animal, a vehicle, or the like. In some implementations, the environment corresponds to a current setting within a theatrical scene of the source content. In some implementations, the source content corresponds to a live image stream, a locally captured image stream, a remotely captured image stream, a movie, a TV episode, or the like. As one example, the source content corresponds to pre-existing images/video. As another example, the source content corresponds to a live image/video stream. 
     As represented by block  7 - 2 , the method  700  includes generating a first environment characterization vector including a plurality of information portions characterizing the environment. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the content analyzer  420 ) generates, based on the source content  415 , a first environment characterization vector  433  for the environment including a plurality of information portions characterizing the environment. In some implementations, generating the first characterization vector includes performing image segmentation, instance segmentation, and/or the like on the source content. 
     In some implementations, the characterization information portions include at least one of information characterizing a first set of objects within the environment (e.g., entities or obstacles) or information characterizing a first set of environmental conditions. (e.g., the plurality of environmental characterization information portions corresponds to objects within the environment, obstacles within the environment, environmental conditions (e.g., weather), and/or the like). For example, with reference to  FIG.  4 B , the first environment characterization vector  433  characterizes an initial state of the environment, where the environment corresponds to a theatrical scene or the like within the source content  415 . As shown in  FIG.  4 B , the first environment characterization vector  433  includes: a spatial representation of the environment  461  (e.g., a point cloud, 3 D model, and/or the like); one or more object representations  462  (e.g., 3 D models) associated with entities, objects, obstacles, content, etc. recognized within the environment; one or more object labels  463  associated with the entities, objects, obstacles, content, etc. recognized within the initial state of the environment; environmental conditions  464  associated with the initial state of the environment (e.g., ambient audio information, ambient lighting information, weather parameters, etc.); and miscellaneous information  465  associated with the initial state of the environment. 
     In some implementations, the method  700  includes generating a three-dimensional (3 D) model associated with the entity based on the source content, wherein the virtual agent corresponds to the 3 D model. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the content analyzer  420 ) generates, based on the source content  415 , a 3 D model  422 A for the entity based on photogrammetry or other computer vision techniques. 
     In some implementations, the method  700  includes generating an initial behavior model for the entity based on the source content, wherein the first set of behavioral trajectories are generated based on the initial behavior model and the first environment characterization vector, and wherein the second set of behavioral trajectories are generated based on the initial behavior model and the second environment characterization vector. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the content analyzer  420 ) generates, based on the source content  415 , an initial behavior model  424 A for the entity based on behavior pattern recognition techniques. 
     As represented by block  7 - 3 , the method  700  includes generating a first set of behavioral trajectories associated with the one or more actions of the entity based on the source content and the first characterization vector for the environment. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the trajectory generator  440 A) generates a first set of behavioral trajectories  442  associated with the one or more actions of the entity with the source content  415  based on the initial behavior model  424 A and the first characterization vector for the environment  433 . 
     In some implementations, the first set of behavioral trajectories are extracted from pre-existing content such as remotely and/or locally captured image streams of a physical entity in the real world. As such, for example, the virtual agent corresponds to a virtual hummingbird that will be trained to imitate a real-life hummingbird based on videos or source content thereof. In some implementations, each of the first set of behavioral trajectories correspond to physical motion plans (PMPs). For example, a PMP includes positional information, angular information, torque information, velocity/acceleration information, etc. for each joint or articulable portion of the entity. 
     As represented by block  7 - 4 , the method  700  includes generating a second environment characterization vector for the environment by perturbing at least some of the plurality of characterization information portions. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the perturbation engine  434 ) generates a second environment characterization vector  435  for the environment by perturbing (or modifying) at least some of the plurality of characterization information portions of the first environment characterization vector  433 . In some implementations, the perturbation engine  434  perturbs (or modifies) at least some of the plurality of characterization information portions of the first environment characterization vector  433  based on a random, pseudo-random, deterministic, procedural, and/or a like process. In some implementations, the perturbation engine  434  accepts user inputs, such as from a content developer, as perturbations to be applied onto the first environmental characterization vector  433 . 
     In some implementations, perturbing at least some of the plurality of environmental characterization information portions includes adding, removing, or modifying at least some objects within the first set of objects. As one example, if the subject object or obstacle corresponds to a tree, the perturbation engine  434  may add, remove, etc. branches, leaves, and/or the like of the tree. As another example, if the subject object or obstacle corresponds to a tree, the perturbation engine  434  may change the size, shape, etc. of the branches, leaves, and/or the like of the tree. 
     In some implementations, perturbing at least some of the plurality of environmental characterization information portions includes modifying at least some environmental conditions associated with the first set of environmental conditions. For example, the perturbation engine  434  may change the wind speed, lighting conditions, temperature, barometric pressure, humidity, precipitation, and/or the like associated with the environment. 
     As one example,  FIG.  5 A  illustrates an initial state of the environment  510 A and a modified state of the environment  510 B. For example, the initial state of the environment  510 A corresponds to an image or portion of the source content  415 , and the modified state of the environment  510 B corresponds to a perturbed or artificially modified version of the initial state of the environment  510 A. In the modified state of the environment  510 B, the modified representation of the date palm  524 B includes more fronds—ten in total as compared to five fronds. As such, in this example, the representation of the entity  522 B modifies its trajectory or path to gather food (e.g., dates) from the modified representation of the date palm  524 B. 
     As another example,  FIG.  5 B  illustrates an initial state of the environment  540 A and a modified state of the environment  540 B. For example, the initial state of the environment  540 A corresponds to an image or portion of the source content  415 , and the modified state of the environment  540 B corresponds to a perturbed or artificially modified version of the initial state of the environment  540 A. In the modified state of the environment  514 B, the modified representation of the wall  554 B includes two additional parallel wings extending along an x-axis. As such, in this example, the representation of the entity  552 B modifies its trajectory or path when locomoting around the modified representation of the wall  554 B to pick up the object  556 . 
     As represented by block  7 - 5 , the method  700  includes generating a second set of behavioral trajectories associated with one or more potential actions of the entity based on the source content and the second characterization vector for the environment. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the trajectory generator  440 B) generates generate a second set of behavioral trajectories  444  associated with one or more potential actions of the entity based on the initial behavior model  424 A and the second characterization vector for the environment  435 . In some implementations, each of the second set of behavioral trajectories correspond to PMPs. 
     As represented by block  7 - 6 , the method  700  includes training a behavior model for a virtual agent based on the first and second sets of behavioral trajectories in order to imitate the entity. With reference to  FIG.  4 A , the training architecture  400  or a component thereof (e.g., the ML system  450 ) generates a trained behavior model  451 A based on the first set of behavioral trajectories  442  and the second set of behavioral trajectories  444 . In some implementations, the ML system  450  corresponds to an NN, a CNN, a DNN, a RNN, an SVM, an RVM, a random forest algorithm, or the like. With continued reference to  FIG.  4 A , the reward estimator  452  generates a reward signal  453  (e.g., a confidence or quality score) for the trained behavior model  451 A and sends the reward signal  453  to the adjustor  454 . As shown in  FIG.  4 A , the adjustor  454  adjusts one or more operating parameters  455  (e.g., filter weights, neurons, etc.) of the ML system  450  in accordance with a determination that the reward signal  453  fails to satisfy a threshold convergence metric. As shown in  FIG.  4 A , the adjustor  454  forgoes adjusting the one or more operating parameters  455  of the ML system  450  in accordance with a determination that the reward signal  453  satisfies the threshold convergence metric. As shown in  FIG.  4 A , the training architecture  400  stores the trained behavior model  451 A in the entry associated with the entity within the VA library  430  when the reward signal  453  satisfies the threshold convergence metric. 
     As represented by block  7 - 7 , the method  700  includes presenting, via the display device, the virtual agent performing one or more actions based on the trained behavior model. With reference to  FIG.  6   , the runtime architecture  600  or a component thereof (e.g., the content selector  612 ) obtains (e.g., receives, retrieves, etc.) a virtual agent from the VA library  430  based on one or more user inputs  601  (e.g., selecting the virtual agent from a menu of virtual agents). With continued reference to  FIG.  6   , the runtime architecture  600  or a component thereof (e.g., the animation engine  620 ) generates an animation  621  including the VA performing one or more actions based on the 3 D model  431 A and the trained behavior model  451 A. 
     Continuing with this example, the runtime architecture  600  or a component thereof (e.g., the pose determiner  652 ) determines a current camera pose of the electronic device  120  and/or the user  150  relative to an origin location for the virtual agent. Continuing with this example, the runtime architecture  600  or a component thereof (e.g., the renderer  654 ) renders the XR environment including the animation of the VA according to the current camera pose relative thereto. According to some implementations, the pose determiner  652  updates the current camera pose in response to detecting translational and/or rotational movement of the electronic device  120  and/or the user  150 . Continuing with this example, in video pass-through scenarios, the runtime architecture  600  or a component thereof (e.g., the compositor  664 ) obtains (e.g., receives, retrieves, etc.) one or more images of the physical environment  105  captured by the image capture device  370  and composites the XR environment including the animation of virtual agent with the one or more images of the physical environment  105  to produce one or more rendered image frames. Finally, the runtime architecture  600  or a component thereof (e.g., the presenter  670 ) presents or causes presentation of the one or more rendered image frames (e.g., via the one or more displays  312  or the like). One of ordinary skill in the art will appreciate that the operations of the optional compositor  664  may not be applicable for fully virtual environments or optical see-through scenarios. 
     In some implementations, the display device corresponds to a transparent lens assembly, and wherein the virtual agent is projected onto the transparent lens assembly. In some implementations, the display device corresponds to a near-eye system, and wherein presenting the virtual agent includes compositing the virtual agent with one or more images of a physical environment captured by an exterior-facing image sensor. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
     It will also be understood that, although the terms “first”, “second”, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first media item could be termed a second media item, and, similarly, a second media item could be termed a first media item, which changing the meaning of the description, so long as the occurrences of the “first media item” are renamed consistently and the occurrences of the “second media item” are renamed consistently. The first media item and the second media item are both media items, but they are not the same media item. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Metadata:
Filing Date: 20211221
Publication Date: 20231024
Grant Date: 20231024
Priority Date: 20210121
Inventors: AHN, EDWARD S.
SIVAPURAPU, Siva Chandra Mouli
DRUMMOND, MARK
MANGLIK, AASHI
BUDHRAM, Shaun
MAHASSENI, BEHROOZ
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N20/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T17/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T17/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/092", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/006", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/0442", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/0464", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/10", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N5/01", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V10/82", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V10/7747", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T17/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 88421096