PATENT DOCUMENT

Publication Number: US-11593982-B1
Application Number: US-202117557273-A
Country: US
Kind Code: B1

Title: Method and device for generating a blended animation

Abstract:
In one implementation, a method for generating a blended animation. The method includes: obtaining a motion input vector for a current time period; generating a motion output vector and pose information for the current time period based on the motion input vector; selecting an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; obtaining a blending coefficients vector for the current time period; generating a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and generating a reward signal for the blended animation for the current time period.

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 a motion input vector for a current time period; 
 generating a motion output vector and pose information for the current time period based on the motion input vector; 
 selecting an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; 
 obtaining a blending coefficients vector for the current time period; 
 generating a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and 
 generating a reward signal for the blended animation for the current time period. 
 
 
     
     
       2. The method of  claim 1 , further comprising:
 in accordance with a determination that the reward signal for the blended animation does not satisfy a threshold value, adjusting one or more tunable parameters for a subsequent time period; and 
 in accordance with a determination that the reward signal for the blended animation satisfies the threshold value, forgoing adjusting one or more tunable parameters. 
 
     
     
       3. The method of  claim 1 , wherein the motion output vector and the pose information for the current time period are generated via a machine learning system. 
     
     
       4. The method of  claim 1 , wherein the motion input vector includes at least one of a task, an objective, a goal, a position information, or rotation information for particular extended reality (XR) content. 
     
     
       5. The method of  claim 4 , wherein the particular XR content corresponds to one of a virtual agent or an animatable XR object. 
     
     
       6. The method of  claim 4 , further comprising:
 presenting, via the display device, the particular XR content performing the blended animation. 
 
     
     
       7. The method of  claim 6 , wherein the display device corresponds to a transparent lens assembly, and wherein the particular XR content is projected onto the transparent lens assembly. 
     
     
       8. The method of  claim 6 , wherein the display device corresponds to a near-eye system, and wherein presenting particular XR content includes compositing particular XR content with one or more images of a physical environment captured by an exterior-facing image sensor. 
     
     
       9. The method of  claim 1 , wherein the motion output vector includes at least one of a change of position, a change of rotation, a change of velocity, or a change of acceleration for particular extended reality (XR) content. 
     
     
       10. The method of  claim 1 , wherein the motion output vector includes at least one of a change of position, a change of rotation, a change of velocity, or a change of acceleration for actuatable components of particular extended reality (XR) content. 
     
     
       11. The method of  claim 10 , wherein the actuatable components corresponds to one of a joint, a limb, or a body segment of the particular XR content. 
     
     
       12. The method of  claim 1 , wherein the reward signal for the blended animation corresponds to one or more of a smoothness factor or a jitter factor. 
     
     
       13. The method of  claim 1 , wherein the reward signal for the blended animation is generated based on a predefined reward function. 
     
     
       14. The method of  claim 1 , wherein the animated motion includes at least one of a fine-grained facial expression or a fine-grained body pose. 
     
     
       15. The method of  claim 1 , wherein the blending coefficients vector includes weights for the motion output vector and the animated motion. 
     
     
       16. The method of  claim 1 , wherein the blending coefficients vector includes weights for the motion output vector and the animated motion for each actuatable component. 
     
     
       17. The method of  claim 1 , wherein the blended animation is generated based on one of a linear blending technique, a spatial blending technique, a temporal blending technique, or a spatial-temporal blending technique. 
     
     
       18. The method of  claim 1 , further comprising:
 identifying at least one portion of the blended animation that exceeds a motion limit; 
 providing a feedback signal that identifies the at least one identified portion of the blended animation; and 
 adjusting one or more tunable parameters based on the feedback signal associated with the blended animation. 
 
     
     
       19. The method of  claim 1 , further comprising:
 identifying at least one portion of the blended animation that exceeds a motion limit; 
 providing a feedback signal that identifies the at least one identified portion of the blended animation; and 
 adjusting the at least one identified portion of the blended animation based on the feedback signal. 
 
     
     
       20. 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 a motion input vector for a current time period; 
 generate a motion output vector and pose information for the current time period based on the motion input vector; 
 select an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; 
 obtain a blending coefficients vector for the current time period; 
 generate a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and 
 generate a reward signal for the blended animation for the current time period. 
 
 
     
     
       21. The device of  claim 20 , wherein the one or more programs further cause the device to:
 in accordance with a determination that the reward signal for the blended animation does not satisfy a threshold value, adjust one or more tunable parameters for a subsequent time period; and 
 in accordance with a determination that the reward signal for the blended animation satisfies the threshold value, forgo adjusting one or more tunable parameters. 
 
     
     
       22. The device of  claim 20 , wherein the one or more programs further cause the device to:
 identify at least one portion of the blended animation that exceeds a motion limit; 
 provide a feedback signal that identifies the at least one identified portion of the blended animation; and 
 adjust one or more tunable parameters based on the feedback signal associated with the blended animation. 
 
     
     
       23. 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 a motion input vector for a current time period; 
 generate a motion output vector and pose information for the current time period based on the motion input vector; 
 select an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; 
 obtain a blending coefficients vector for the current time period; 
 generate a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and 
 generate a reward signal for the blended animation for the current time period. 
 
     
     
       24. The non-transitory memory of  claim 23  wherein the one or more programs further cause the device to:
 in accordance with a determination that the reward signal for the blended animation does not satisfy a threshold value, adjust one or more tunable parameters for a subsequent time period; and 
 in accordance with a determination that the reward signal for the blended animation satisfies the threshold value, forgo adjusting one or more tunable parameters. 
 
     
     
       25. The non-transitory memory of  claim 23 , wherein the one or more programs further cause the device to:
 identify at least one portion of the blended animation that exceeds a motion limit; 
 provide a feedback signal that identifies the at least one identified portion of the blended animation; and 
 adjust one or more tunable parameters based on the feedback signal associated with the blended animation.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 63/134,381, filed on Jan. 6, 2021, which is herein incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to animation and rendering and, in particular, to systems, devices, and methods for generating a blended animation. 
     BACKGROUND 
     A machine learning (ML) system may be able to output coarse joint positions/movements for animating a virtual agent. However, the ML system may not be capable of fine-grained movements, such as facial expressions and the like, which may instead be pre-authored or manually crafted. 
    
    
     
       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 animation architecture in accordance with some implementations. 
         FIGS.  4 B and  4 C  illustrate example data structures in accordance with some implementations. 
         FIG.  5    is a block diagram of an example neural network in accordance with some implementations. 
         FIG.  6    is a block diagram of an example rendering architecture in accordance with some implementations. 
         FIG.  7    is a flowchart representation of a method of generating a blended animation 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 generating a blended animation. 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 a motion input vector for a current time period; generating a motion output vector and pose information for the current time period based on the motion input vector; selecting an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; obtaining a blending coefficients vector for the current time period; generating a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and generating a reward signal for the blended animation for the current time period. 
     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 802.11x, IEEE 802.16x, IEEE 802.3x, 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 (3D). 
     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 802.3x, IEEE 802.11x, IEEE 802.16x, 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, blended animation(s), 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, an animation architecture  400  is configured to generate a blended animation for a current time period or image frame by blending a motion output vector from the motion generator  420  with an animated motion selected from the animated motions bank  435  based on a blending coefficients vector from a motion controller  410 . The animation architecture  400  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the animation architecture  400  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the animation architecture  400  includes a motion controller  410 , a motion generator  420 , a pose matching engine  430 , an animated motions bank  435 , a motion blending engine  440 , a reward estimator  450 , and a limiter  452 . 
     In some implementations, the motion controller  410  is configured to generate a motion input vector that is fed to the motion generator  420  and a blending coefficients vector for the blended animation. The motion controller  410  is described in more detail below with reference to  FIG.  4 A . The motion input vector is described in more detail below with reference to  FIG.  4 B , and the blending coefficients vector is described in more detail below with reference to  FIG.  4 C . 
     In some implementations, the motion controller  410  is also configured to receive a reward signal associated with the quality of the blended animation for the current time period or image frame. In accordance with a determination that the reward signal for the blended animation does not satisfy a threshold value, the motion controller  410  adjusts one or more tunable parameters of the motion controller  410  for a subsequent time period. In accordance with a determination that the reward signal for the blended animation satisfies the threshold value, the motion controller  410  forgoes adjusting one or more tunable parameters of the motion controller  410 . To that end, in various implementations, the motion controller  410  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the motion generator  420  is configured to generate a motion output vector and pose information for the current time period or image frame based on the motion input vector. In some implementations, the motion generator  420  corresponds to a machine learning (ML) system such as a neural network (NN), a deep neural network (DNN), a convolutional neural network (CNN), a relevant vector machine (RVM), a support vector machine (SVM), a random forest algorithm, or the like. The motion generator  420  is described in more detail below with reference to  FIG.  4 A . The motion output vector is described in more detail below with reference to  FIG.  4 C . To that end, in various implementations, the motion generator  420  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the pose matching engine  430  is configured to select or identify an animated motion from the animated motions bank  435  that matches the pose information within a threshold tolerance value. The pose matching engine  430  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the pose matching engine  430  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the animated motions bank  435  includes a plurality of pre-existing animated motions. For example, a respective animated motion corresponds to one or more fine-grained facial expressions, body language poses, or the like. In some implementations, the animated motions bank  435  is pre-populated or manually authored by the user  150  or another user. In some implementations, the animated motions bank  435  is located local relative to the controller  110 . In some implementations, the animated motions bank  435  is located remote from the controller  110  (e.g., at a remote server, a cloud server, or the like). 
     In some implementations, the motion blending engine  440  is configured to generate a blended motion for the current time period or image frame by blending the motion output vector with the animated motion selected from the animated motions bank  435  based on the blending coefficients vector. In some implementations, the motion blending engine  440  may perform linear blending, spatial blending, temporal blending, spatial-temporal blending, or the like. The motion blending engine  440  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the motion blending engine  440  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the reward estimator  450  is configured to generate a reward signal (e.g., an animation quality score) for the blended animation for the current time period and send the reward signal to the motion controller  410 . In some implementations, the reward signal for the blended animation corresponds to one or more of a smoothness factor, a jitter factor, and/or the like for the blended animation. The reward estimator  450  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the reward estimator  450  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the limiter  452  is configured to identify motions within the blended animation that are outside of a predefined range (or tolerance) of motions and provide a feedback signal to the motion controller  410  associated therewith. The limiter  452  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the limiter  452  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a content manager  620  is configured to manage and update the layout, setup, structure, and/or the like for the XR content selected by the content selector  622 . The content manager  620  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the content manager  540  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the content manager  620  includes a content selector  622  and the supervisor network  402 . 
     In some implementations, the content selector  622  is configured to select XR content (sometimes also referred to herein as “graphical content” or “virtual content”) from a content library  615  based on one or more user requests and/or inputs (e.g., a voice command, a selection from a user interface (UI) menu of XR content items, and/or the like). The content selector  622  is described in more detail below with reference to  FIG.  6   . To that end, in various implementations, the content selector  622  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the content library  615  includes a plurality of content items such as audio/visual (A/V) content and/or XR content, objects, items, scenery, etc. As one example, the XR content includes 3D reconstructions of user captured videos, movies, TV episodes, and/or other XR content. In some implementations, the content library  615  is pre-populated or manually authored by the user  150 . In some implementations, the content library  615  is located local relative to the controller  110 . In some implementations, the content library  615  is located remote from the controller  110  (e.g., at a remote server, a cloud server, or the like). 
     In some implementations, the supervisor network  402  is configured to update and manage an XR environment for the user that includes the selected XR content performing the blended animation. For example, the blended animation may cause a virtual agent or other XR content to locomote with the XR environment. To that end, in various implementations, the supervisor network  402  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 blended animation. 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 A/V content and/or XR content. 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 A/V content and/or the XR content including the blended animation 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 A/V content and/or XR content 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 content manager  620 , the animation architecture  400 , and the rendering engine  650  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 content manager  620 , the animation architecture  400 , and the rendering engine  650  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 presenter  670 , the interaction handler  610 , 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 presenter  670 , the interaction handler  610 , 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 animation 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. In some implementations, the animation architecture  400  includes software, firmware, hardware, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or the like. 
     As shown in  FIG.  4 A , the motion controller  410  is communicatively coupled with the supervisor network  402 , which manages and updates an XR environment presented via the electronic device  120  to the user  150 . In some implementations, the motion controller  410  generates a motion input vector  411  and a blending coefficients vector  413  for the current time period or image frame based on the current state information  401 . 
       FIG.  4 B  shows example data structures for the current state information  401  and the motion input vector  411  in accordance with some implementations. One of ordinary skill in the art will appreciate that the current state information  401  and the motion input vector  411  shown in  FIG.  4 B  are example data structures that may be formatted differently and otherwise modified in various other implementations. According to some implementation, the current state information  401  describes the state of the XR environment at the end of the previous time period or image frame including any animated content portions. As shown in  FIG.  4 B , the current state information  401  includes: lighting information  462 A associated with the XR environment, audio information  462 B associated with the XR environment, other environmental information  462 C associated with the XR environment (e.g., weather or other transient conditions in the XR environment), and scenery information  464  associated with the XR environment (e.g., identifiers for and/or information associated with stationary and/or non-animated XR content within the XR environment). 
     In  FIG.  4 B , the current state information  401  also includes one or more pairs of information for specific animated content within the XR environment. For example, the specific animated content corresponds to a virtual agent, an XR object, or the like within the XR environment that is animatable or otherwise enabled to translate and/or rotate. As such, a first pair of information includes an animated content identifier  466 A for first animated content (e.g., a serial number or other identifier) and animated content information  468 A for the first animated content. Another pair of information includes an animated content identifier  466 N for Nth animated content and animated content information  468 N for the Nth animated content. As shown in  FIG.  4 B , the animated content information  468 A includes: position and rotation information  482  for the first animated content (e.g., translational and rotational values for the first animated content relative to the XR environment), motion information  484  for the first animated content (e.g., a current heading, a current velocity value, a current acceleration value, and/or the like for the first animated content), and actuatable component (joint) information  486  for the first animated content. For example, the actuatable component (joint) information  486  includes translational and rotational values for each actuatable component of the first animated content, classifiers associated with a type of each joint of the first animated content, current torque or the like values associated with each actuatable component of the first animated content, constraints associated with each actuatable component of the first animated content (e.g., load or stress limit values for each actuatable component, limited freedom of angular movement for each actuatable component, limited freedom of displacement for each actuatable component, etc.), and/or the like. 
     As shown in  FIG.  4 B , the motion input vector  411  includes the animated content identifier  466 A and the animated content information  468 A for first animated content as well as input motion information  470 A for first animated content. In  FIG.  4 B , the input motion information  470 A includes a trajectory  492  for first animated content, a goal/task/objective  494  for first animated content, and miscellaneous information  496  for first animated content. One of ordinary skill in the art will appreciate that multiple instances of animated content may be managed in parallel for a respective time period or image frame. Thus, although the motion input vector  411  refers to a single instance of animated content, one of ordinary skill in the art will appreciate the motion input vector  411  may include additional information when managing multiple instances of animated content. 
       FIG.  4 C  shows example alternative data structures for blending coefficients vectors  413 A and  413 B according to some implementations. One of ordinary skill in the art will appreciate that the blending coefficients vectors  413 A and  413 B shown in  FIG.  4 C  are example data structures that may be formatted differently and otherwise modified in various other implementations. As shown in  FIG.  4 C , the blending coefficients vector  413 A includes the animated content identifier  466 A and an overall animation weight information  482 A, including a blending ratio  483  specifying the weights given to the motion output vector  423  and the animated motion  431  for the motion blending process performed by the motion blending engine  440 . In this example, the overall animation weight information  482 A relates to the animated content as a whole (e.g., a virtual agent or other XR content identified by the animated content identifier  466 A). 
     As shown in  FIG.  4 C , the blending coefficients vector  413 B includes the animated content identifier  466 A and actuatable component specific animation weights. In  FIG.  4 C , the blending coefficients vector  413 B includes pairs of information on an actuatable component basis. For example, a first pair of information including an actuatable component identifier  472 A (e.g., a serial number or other identifier) for a first actuatable component of the animated content and animation weight information  474 A for a first actuatable component of the animated content, and an Nth pair of information including an actuatable component identifier  472 N (e.g., a serial number or other identifier) for the Nth actuatable component of the animated content and animation weight information  474 N for the Nth actuatable component of the animated content. For example, the animation weight information  474 A includes a blending ratio  475 A specifying the weights given to the motion output vector  423  and the animated motion  431  with respect to the actuatable component  472 A for the motion blending process performed by the motion blending engine  440 . In some implementations, the blending ratios  483  and  475 A are predetermined, tunable, deterministic, pseudo-random, or the like. One of ordinary skill in the art will appreciate that the blending coefficients vectors  413 A and  413 B relate to different implementations where motion blending may occur on a whole animated content basis or on an actuatable component basis. 
     As shown in  FIG.  4 A , the motion generator  420  generates pose information  421  and a motion output vector  423  based on the motion input vector  411  from the motion controller  410 . In some implementations, the motion generator  420  corresponds to an ML system such as an NN, a DNN, a CNN, a RVM, an SVM, a random forest algorithm, or the like. In some implementations, the pose information  421  includes positional and/or translational information for the overall animated content. In some implementations, the pose information  421  includes positional and/or translational information for each actuatable component (e.g., a joint, a limb, a body segment, or the like) of the animated content. 
       FIG.  4 C  shows example data structures for the motion output vector  423  in accordance with some implementations. One of ordinary skill in the art will appreciate that the motion output vector  423  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 motion output vector  423  includes the animated content identifier  466 A and the animated content information  468 A for first animated content as well as output motion information  471 A for first animated content. In  FIG.  4 C , the output motion information  471 A includes a change of position  4102 , a change of rotation  4104 , a change of velocity and/or acceleration  4106 , and miscellaneous information  4108  for first animated content. For example, the output motion information  471 A may be associated with the whole of the first animated content or individual actuatable components (e.g., joints, limbs, body segments, or the like) of the first animated content. One of ordinary skill in the art will appreciate that multiple instances of animated content may be managed in parallel for a respective time period or image frame. Thus, although the motion output vector  423  refers to a single instance of animated content, one of ordinary skill in the art will appreciate the motion output vector  423  may include additional information when managing multiple instances of animated content. 
     As shown in  FIG.  4 A , the pose matching engine  430  selects or identifies an animated motion  431  from the animated motions bank  435  for the current time period or image frame that matches the pose information  421  within a threshold tolerance value (e.g., X mm for positional values within the pose information  421 , and Y° for rotational values within the pose information  421 , or the like). 
     As shown in  FIG.  4 A , the motion blending engine  440  generates a blended animation  441  for the current time period or image frame by blending the motion output vector  423  with the animated motion  431  selected from the animated motions bank  435  based on the blending coefficients vector  413 . For example, as described above, the blending coefficients vector  413  includes a blending ratio with weights for the motion output vector  423  and the animated motion  431 . In some implementations, the motion blending engine  440  performs linear blending, spatial blending, temporal blending, spatial-temporal blending, or the like to generate the blended animation  441 . 
     As shown in  FIG.  4 A , the reward estimator  450  generates a reward signal  451  (e.g., an animation quality score) for the blended animation  441  for the current time period. In some implementations, the reward estimator  450  may include a reward function adjustor mechanism for adjusting the reward function that produces the reward signal  451 . 
     As shown in  FIG.  4 A , the limiter  452  identifies motions within the blended animation  441  that are outside of a predefined range (or tolerance) of motions and generates a feedback signal  453  associated therewith. As one example, if the blended animation  441  includes an action that is outside of a predefined set of actions available to the animated content, the limiter  452  may stop the action from occurring or recommend an alternative action. As another example, if the blended animation  441  includes a motion that involves a positional displacement, an angular movement, or the like that is outside of a pre-defined range of motion, the limiter  452  may stop the motion from occurring or recommend a scaled-back motion that is within the pre-defined range of motion. 
     As shown in  FIG.  4 A , the motion controller  410  receives the reward signal  451  associated with the quality of the blended animation  441  for the current time period or image frame. In accordance with a determination that the reward signal  451  for the blended animation  441  does not satisfy a threshold value, the motion controller  410  adjusts one or more tunable parameters of the motion controller  410  (e.g., the blending coefficients vector  413 , the motion input vector  411 , and/or the like) for a subsequent time period. In accordance with a determination that the reward signal  451  for the blended animation  441  satisfies the threshold value, the motion controller  410  forgoes adjusting one or more tunable parameters of the motion controller  410 . 
       FIG.  5    is a block diagram of an example neural network  500  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  500  may correspond to the motion generator  420  in  FIGS.  2  and  4 A . To that end, as a non-limiting example, in some implementations, the neural network  500  includes an input layer  520 , a first hidden layer  522 , a second hidden layer  524 , and an output layer  526 . While the neural network  500  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  520  is coupled (e.g., configured) to receive an input  540 . For example, with reference to  FIG.  4 A , the input  540  corresponds to the motion input vector  411  from the motion controller  410 . In various implementations, the input layer  520  includes a number of long short-term memory (LSTM) logic units  520   a  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  520   a  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  522  includes a number of LSTM logic units  522   a  or the like. As illustrated in the example of  FIG.  5   , the first hidden layer  522  receives its inputs from the input layer  520 . For example, the first hidden layer  522  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 second hidden layer  524  includes a number of LSTM logic units  524   a  or the like. In some implementations, the number of LSTM logic units  524   a  is the same as or similar to the number of LSTM logic units  520   a  in the input layer  520  or the number of LSTM logic units  522   a  in the first hidden layer  522 . As illustrated in the example of  FIG.  5   , the second hidden layer  524  receives its inputs from the first hidden layer  522 . Additionally, and/or alternatively, in some implementations, the second hidden layer  524  receives its inputs from the input layer  520 . For example, the second hidden layer  524  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  526  includes a number of LSTM logic units  526   a  or the like. In some implementations, the number of LSTM logic units  526   a  is the same as or similar to the number of LSTM logic units  520   a  in the input layer  520 , the number of LSTM logic units  522   a  in the first hidden layer  522 , or the number of LSTM logic units  524   a  in the second hidden layer  524 . In some implementations, the output layer  526  is a task-dependent layer that performs motion related tasks. In some implementations, the output layer  526  includes an implementation of a multinomial logistic function (e.g., a soft-max function) that produces an output  542 . For example, with reference to  FIG.  4 A , the output  542  corresponds to the pose information  421  and the motion output vector  423 . 
     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.  6    is a block diagram of an example rendering 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 rendering 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. In some implementations, the rendering architecture  600  includes software, firmware, hardware, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or the like. 
     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/V content and/or XR content for presentation. For example, the one or more user inputs  601  correspond to a gestural input selecting XR content from a UI menu detected via hand tracking, an eye gaze input selecting XR content from the UI menu detected via eye tracking, a voice command selecting XR content from the UI menu detected via a microphone, and/or the like. In some implementations, the content selector  622  selects XR content  621  from the content library  615  based on one or more user inputs  601  (e.g., a voice command, a selection from a menu of XR content items, and/or the like). For example, the XR content  621  corresponds to a virtual agent, an XR object, or the like within the XR environment that is animatable or otherwise enabled to translate and/or rotate. 
     In various implementations, the content manager  620  or a component thereof (e.g., the supervisor network  402 ) manages and updates the layout, setup, structure, and/or the like of the XR environment as the animation architecture  400  animates the XR content  621 . As a result, the content manager  620  or a component thereof (e.g., the supervisor network  402 ) updates the current state information  401  and provides the current state information  401  to the animation architecture  400 . According to some implementations, as described above with reference to  FIG.  4 A , the animation architecture  400  generates a blended animation  441  associated with the XR content  621  for a current time period or image frame based at least in part on the current state information  401 . 
     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 XR content  621  and/or the physical environment  105 . In some implementations, the renderer  654  renders the XR content  621  performing the blended animation  441  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 content 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 generating a blended animation 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 a non-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. 
     As discussed above, a machine learning (ML) system may be able to output coarse joint positions/movements for animating a virtual agent. However, the ML system may not be capable of fine-grained movements, such as facial expressions and the like, which may instead be pre-authored or manually crafted. As such, in some implementations, a computing system blends motion outputs from a motion generator (e.g., an ML system) with manually crafted motion outputs on a frame-by-frame basis. The computing system also generates a reward signal (e.g., an animation quality score) for the blended animation based on the smoothness thereof. The computing system also perturbs a motion controller that provides the input(s) to the motion generator based on the reward signal in order to improve future blended animations. In other words, the computing system trains (or adjusts) the motion controller to generate quality animations that blend motion outputs from an ML system with manually crafted animations. 
     As represented by block  7 - 1 , the method  700  includes obtaining, from a motion controller (e.g., the motion controller  410  in  FIGS.  2  and  4 A ), a motion input vector for a current time period. With reference to  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the motion controller  410 ) generates a motion input vector for the current time period or image frame.  FIG.  4 B , for example, illustrates the motion input vector  411 . In some implementations, the motion input vector includes at least one of a task, an objective, a goal, position information, or rotation information for particular extended reality (XR) content. In some implementations, the particular XR content corresponds to one of a virtual agent or an animatable XR object. 
     As represented by block  7 - 2 , the method  700  includes generating, via a motion generator (e.g., the motion generator  420  in  FIGS.  2  and  4 A ), a motion output vector and pose information for the current time period based on the motion input vector. In some implementations, the motion generator corresponds to a machine learning (ML) system. For example, the motion ML system corresponds to an NN, a DNN, a CNN, a RVM, an SVM, a random forest algorithm, or the like. 
     With reference to  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the motion generator  420 ) generates a motion output vector  423  and pose information  421  for the current time period or image frame based on the motion input vector.  FIG.  4 C , for example, illustrates the motion output vector  423 . In some implementations, the motion output vector includes at least one of a change of position, a change of rotation, a change of velocity, or a change of acceleration for particular XR content. 
     In some implementations, the motion output vector includes at least one of a change of position, a change of rotation, a change of velocity, or a change of acceleration for actuatable components of particular XR content. In some implementations, the actuatable components corresponds to one of a joint, a limb, or a body segment of the particular XR content. 
     As represented by block  7 - 3 , the method  700  includes selecting an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value. With reference to  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the pose matching engine  430 ) selects or identifies an animated motion  431  from the animated motions bank  435  that matches the pose information  421  within a threshold tolerance value. In some implementations, the animated motion selected from the bank of animated motions includes at least one of a fine-grained facial expression or a fine-grained body pose. For example, the animated motion includes fine-grained motions for body language purposes or the like. 
     As represented by block  7 - 4 , the method  700  includes obtaining, from the motion controller (e.g., the motion controller  410  in  FIGS.  2  and  4 A ), a blending coefficients vector for the current time period. With reference to  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the motion controller  410 ) generates a blending coefficients vector  413  for the current time period or image frame. In some implementations, the blending coefficients vector includes weights for the motion output vector and the animated motion. In some implementations, the blending coefficients vector includes weights for the motion output vector and the animated motion for each actuatable component. 
       FIG.  4 C  shows example alternative data structures for blending coefficients vectors  413 A and  413 B according to some implementations. One of ordinary skill in the art will appreciate that the blending coefficients vectors  413 A and  413 B shown in  FIG.  4 C  are example data structures that may be formatted differently and otherwise modified in various other implementations. As shown in  FIG.  4 C , the blending coefficients vector  413 A includes the animated content identifier  466 A and an overall animation weight information  482 A, which specifies a blending ratio  483  specifying the weights given to the motion output vector  423  and the animated motion  431  for the motion blending process performed by the motion blending engine  440 . As shown in  FIG.  4 C , the blending coefficients vector  413 B includes the animated content identifier  466 A and actuatable component specific animation weights. 
     As represented by block  7 - 5 , the method  700  includes generating a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector. With reference to  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the motion blending engine  440 ) generates a blended animation  441  for the current time period or image frame by blending the motion output vector  423  with the animated motion  431  selected from the animated motions bank  435  based on the blending coefficients vector  413 . For example, as described above, the blending coefficients vector  413  includes a blending ratio with weights for the motion output vector  423  and the animated motion  431 . In some implementations, the blended animation is generated based on one of a linear blending technique, a spatial blending technique, a temporal blending technique, or a spatial-temporal blending technique. 
     In some implementations, the method  700  includes presenting, via the display device, the particular XR content performing the blended animation. For example, with reference to  FIG.  6   , the computing system or a component thereof (e.g., the content selector  622 ) obtains (e.g., receives, retrieves, etc.) XR content  621  from the content library  615  based on one or more user inputs  601  (e.g., selecting the XR content  621  from a menu of XR content items). Continuing with this example, the computing system 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 XR content  621 . Continuing with this example, the computing system or a component thereof (e.g., the renderer  654 ) renders the XR content  621  performing the blended animation  441  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 computing system 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 rendered XR content  621  with the one or more images of the physical environment  105  to produce one or more rendered image frames. Finally, the computing system or a component thereof (e.g., the A/V 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 XR content is projected onto the transparent lens assembly. In some implementations, the display device corresponds to a near-eye system, and wherein presenting the XR content includes compositing the XR content with one or more images of a physical environment captured by an exterior-facing image sensor. 
     As represented by block  7 - 6 , the method  700  includes generating a reward signal (e.g., an animation quality score) for the blended animation for the current time period. With reference to  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the reward estimator  450 ) generates a reward signal  451  (e.g., an animation quality score) for the blended animation  441  for the current time period or image frame. In some implementations, the reward estimator  450  may include a reward function adjustor mechanism for adjusting the reward function that produces the reward signal  451 . In some implementations, the reward signal for the blended animation corresponds to one or more of a smoothness factor or a jitter factor. In some implementations, the reward signal for the blended animation is generated based on a predefined reward function. 
     In some implementations, as represented by blocks  7 - 7  and  7 - 8 , the method  700  further includes: in accordance with a determination that the reward signal for the blended animation does not satisfy a threshold value, adjusting one or more tunable parameters of the motion controller for a subsequent time period; and in accordance with a determination that the reward signal for the blended animation satisfies the threshold value, forgoing adjusting one or more tunable parameters of the motion controller. For example, as shown in  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the motion controller  410 ) receives the reward signal  451  associated with the quality of the blended animation  441  for the current time period or image frame. In accordance with a determination that the reward signal  451  for the blended animation  441  does not satisfy a threshold value, the animation architecture  400  or a component thereof (e.g., the motion controller  410 ) adjusts one or more tunable parameters of the motion controller  410  (e.g., in order to modify the blending coefficients vector  413 , the motion input vector  411 , and/or the like) for a subsequent time period. In accordance with a determination that the reward signal  451  for the blended animation  441  satisfies the threshold value, the animation architecture  400  or a component thereof (e.g., the motion controller  410 ) forgoes adjusting one or more tunable parameters of the motion controller  410 . 
     In some implementations, the method  700  further includes: identifying at least one portion of the blended animation that exceeds a motion limit; providing a feedback signal to the motion controller that identifies the at least one identified portion of the blended animation; and adjusting one or more tunable parameters of the motion controller based on the feedback signal associated with the blended animation. 
     In some implementations, the method  700  further includes: identifying at least one portion of the blended animation that exceeds a motion limit; providing a feedback signal to the motion controller that identifies the at least one identified portion of the blended animation; and adjusting, via the motion controller, the at least one identified portion of the blended animation based on the feedback signal. For example, as shown in  FIG.  4 A , the animation architecture  400  or a component thereof (e.g., the limiter  452 ) identifies motions within the blended animation  441  that are outside of a predefined range (or tolerance) of motions and generates a feedback signal  453  associated therewith. As one example, if the blended animation  441  includes an action that is outside of a predefined set of actions available to the animated content, the limiter  452  may stop the action from occurring or recommend an alternative action. As another example, if the blended animation  441  includes a motion that involves a positional displacement, an angular movement, or the like that is outside of a pre-defined range of motion, the limiter  452  may stop the motion from occurring or recommend a scaled-back motion that is within the pre-defined range of motion. 
     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: 20230228
Grant Date: 20230228
Priority Date: 20210106
Inventors: MAHASSENI, BEHROOZ
MANGLIK, AASHI
DRUMMOND, MARK
AHN, EDWARD S.
BUDHRAM, Shaun
SIVAPURAPU, Siva Chandra Mouli
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F3/011", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T13/40", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/40", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/0442", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/0464", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/08", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N20/10", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N20/20", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N5/01", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 85289457