PATENT DOCUMENT

Publication Number: US-10782779-B1
Application Number: US-201916580372-A
Country: US
Kind Code: B1

Title: Feedback coordination for a virtual interaction

Abstract:
In some implementations, a method includes: obtaining user movement information, wherein the user movement information characterizes real-world body pose and trajectory information of the user; generating, from real-world user movement information and a predetermined placement of the virtual instrument in the computer generated reality (CGR) environment, a predicted virtual instrument interaction time for a virtual instrument interaction prior to the virtual instrument interaction occurring; determining whether or not the predicted virtual instrument interaction time falls within an acceptable temporal range around one of a plurality of temporal sound markers; and in response to determining that the predicted virtual instrument interaction time falls within the acceptable temporal range around a particular temporal sound marker of the plurality of temporal sound markers, quantizing the virtual instrument interaction by presenting play of the virtual instrument to match the particular temporal sound marker of the plurality of temporal sound markers.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a device including one or more processors, non-transitory memory, and one or more user interaction hardware components configured to enable a user to play a virtual instrument in a computer-generated reality (CGR) environment: 
 obtaining user movement information, wherein the user movement information characterizes real-world body pose and trajectory information of the user; 
 generating, from real-world user movement information and a predetermined placement of the virtual instrument in the CGR environment, a predicted virtual instrument interaction time for a virtual instrument interaction prior to the virtual instrument interaction occurring; 
 determining whether or not the predicted virtual instrument interaction time falls within an acceptable temporal range around one of a plurality of temporal sound markers; and 
 in response to determining that the predicted virtual instrument interaction falls within the acceptable temporal range around a particular temporal sound marker of the plurality of temporal sound markers, quantizing the virtual instrument interaction by presenting play of the virtual instrument to match the particular temporal sound marker of the plurality of temporal sound markers. 
 
     
     
       2. The method of  claim 1 , wherein the predetermined placement of the virtual instrument in the CGR environment includes coordinates of at least one surface of the virtual instrument in the CGR environment. 
     
     
       3. The method of  claim 1 , wherein the one or more user interaction hardware components include a head-mountable display. 
     
     
       4. The method of  claim 1 , wherein the one or more user interaction hardware components include one or more hand-held devices. 
     
     
       5. The method of  claim 1 , wherein the user movement information includes information characterizing a velocity of user interaction with at least one user interaction hardware component of the one or more user interaction hardware components. 
     
     
       6. The method of  claim 1 , wherein the user movement information includes information characterizing an acceleration of user interaction with at least one user interaction hardware component of the one or more user interaction hardware components. 
     
     
       7. The method of  claim 1 , wherein the user movement information includes information characterizing a direction of user gaze. 
     
     
       8. The method of  claim 1 , wherein obtaining the user movement information includes determining the user movement information. 
     
     
       9. The method of  claim 1 , wherein determining the user movement information comprises determining the user movement information using one or more image sensors. 
     
     
       10. The method of  claim 9 , wherein the one or more image sensors include a forward-facing camera, wherein the forward-facing camera is configured to capture images from a point of view characterized by an axis substantially parallel to an axis characterizing a point of view of the user. 
     
     
       11. The method of  claim 9 , wherein the one or more image sensors include a non-forward-facing camera, wherein the non-forward-facing camera is configured to capture images from a point of view characterized by an axis substantially perpendicular to an axis characterizing a point of view of the user. 
     
     
       12. The method of  claim 11 , wherein the non-forward-facing camera is a downward-facing camera. 
     
     
       13. The method of  claim 1 , further comprising:
 obtaining one or more images captured by one or more image sensors; and 
 processing the one or more images to extract one or more image features; and 
 wherein determining the one or more user movement information includes determining information characterizing real-world body pose and trajectory information of the user from the one or more image features. 
 
     
     
       14. The method of  claim 1 , wherein determining the user movement information comprises determining the user movement information using an inertial measurement unit in at least one user interaction hardware component of the one or more user interaction hardware components. 
     
     
       15. The method of  claim 1 , wherein the acceptable temporal range is determined based on data characterizing past user interaction with the device. 
     
     
       16. The method of  claim 1 , further comprising:
 determining the acceptable temporal range around each sound marker of the plurality of temporal sound markers. 
 
     
     
       17. The method of  claim 1 , wherein presenting play of the virtual instrument comprises producing one or more CGR feedbacks. 
     
     
       18. The method of any of  claim 17 , wherein the one or more CGR feedbacks include an audio feedback. 
     
     
       19. The method of any of  claim 17 , wherein the one or more CGR feedbacks include a video feedback. 
     
     
       20. The method of any of  claim 17 , wherein the one or more CGR feedbacks include a haptic feedback. 
     
     
       21. The method of  claim 1 , wherein the device is configured to translate the user movement information into a virtual instrument interaction. 
     
     
       22. The method of  claim 21 , wherein translating the user movement information into a virtual instrument interaction includes determining that real-world body pose and trajectory information of the user falls within a three-dimensional region associated with a particular virtual instrument interaction. 
     
     
       23. A device comprising:
 one or more processors; 
 a non-transitory memory; 
 one or more user interaction hardware components configured to enable a user to play a virtual instrument in a computer-generated reality (CGR) environment; 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 user movement information, wherein the user movement information characterizes real-world body pose and trajectory information of the user; 
 generate, from real-world user movement information and a predetermined placement of the virtual instrument in the CGR environment, a predicted virtual instrument interaction time for a virtual instrument interaction prior to the virtual instrument interaction occurring; 
 determine whether or not the predicted virtual instrument interaction time falls within an acceptable temporal range around one of a plurality of temporal sound markers; and 
 in response to determining that the predicted virtual instrument interaction falls within the acceptable temporal range around a particular temporal sound marker of the plurality of temporal sound markers, quantize the virtual instrument interaction by presenting play of the virtual instrument to match the particular temporal sound marker of the plurality of temporal sound markers. 
 
     
     
       24. The device of  claim 23 , wherein the one or more programs further cause the device to:
 obtain one or more images captured by one or more image sensors; and 
 process the one or more images to extract one or more image features; and 
 wherein determining the one or more user movement information includes determining information characterizing real-world body pose and trajectory information of the user from the one or more image features. 
 
     
     
       25. The device of  claim 23 , wherein determining the user movement information comprises determining the user movement information using an inertial measurement unit in at least one user interaction hardware component of the one or more user interaction hardware components. 
     
     
       26. The device of  claim 23 , wherein the acceptable temporal range is determined based on data characterizing past user interaction with the device. 
     
     
       27. A non-transitory memory storing one or more programs, which, when executed by one or more processors of a device with one or more user interaction hardware components configured to enable a user to play a virtual instrument in a computer-generated reality (CGR) environment, cause the device to:
 obtain user movement information, wherein the user movement information characterizes real-world body pose and trajectory information of the user; 
 generate, from real-world user movement information and a predetermined placement of the virtual instrument in the CGR environment, a predicted virtual instrument interaction time for a virtual instrument interaction prior to the virtual instrument interaction occurring; 
 determine whether or not the predicted virtual instrument interaction time falls within an acceptable temporal range around one of a plurality of temporal sound markers; and 
 in response to determining that the predicted virtual instrument interaction falls within the acceptable temporal range around a particular temporal sound marker of the plurality of temporal sound markers, quantize the virtual instrument interaction by presenting play of the virtual instrument to match the particular temporal sound marker of the plurality of temporal sound markers. 
 
     
     
       28. The non-transitory memory of  claim 27 , wherein the one or more programs further cause the device to:
 obtain one or more images captured by one or more image sensors; and 
 process the one or more images to extract one or more image features; and 
 wherein determining the one or more user movement information includes determining information characterizing real-world body pose and trajectory information of the user from the one or more image features. 
 
     
     
       29. The non-transitory memory of  claim 27 , wherein determining the user movement information comprises determining the user movement information using an inertial measurement unit in at least one user interaction hardware component of the one or more user interaction hardware components. 
     
     
       30. The non-transitory memory of  claim 27 , wherein the acceptable temporal range is determined based on data characterizing past user interaction with the device.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent App. Nos. 62/737,624, filed on Sep. 27, 2018 and 62/811,996, filed on Feb. 28, 2018, which are incorporated by reference in their entireties. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to computer-generated reality (CGR) environments and in particular to feedback coordination for a virtual interaction. 
     BACKGROUND 
     Music processing systems (such as music processing systems in CGR environments) should ideally strive to improve temporal precision in musical performances in order to enhance music quality and user experience. This task, known as quantization, may involve presenting playback of sound at a time different from a time of performance of the sound by determining that such a modified presentation is more aligned with intentions of the performer and/or the structure of a piece of music. Existing music processing systems continue to face challenges when it comes to effective and timely quantization of live musical performances. 
     A feedback device (such as speaker/headphones, a haptics engine, or the like) often has a predetermined latency between initiation of associated feedback (such as audio, haptics, or the like) and user perception of the associated feedback due to, for example, hardware and/or transmission delays. In turn, feedback associated with a virtual interaction in a CGR environment should ideally strive for life-like coordination (or synchronization) between perception of the feedback and occurrence of the virtual interaction itself. However, existing CGR delivery systems continue to face challenges when it comes to effective and timely coordination (or synchronization) between the feedback and the virtual interaction itself. 
    
    
     
       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 environment 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 head-mounted device (HMD) in accordance with some implementations. 
         FIG. 4  is a block diagram of an example hand-held device in accordance with some implementations. 
         FIGS. 5A and 5B  illustrate example virtual instrument interaction data in accordance with some implementations. 
         FIG. 6  is a flowchart representation of a method of predictive quantization of a virtual instrument interaction in accordance with some implementations. 
         FIG. 7  is a flowchart representation of a method of presenting user play of a virtual musical instrument in accordance with some implementations. 
         FIG. 8  is a flowchart representation of a method of delivering feedback coordinated with a virtual interaction in accordance with some implementations. 
         FIG. 9  is a timing diagram for coordinating feedback from various feedback devices in accordance with some implementations. 
         FIG. 10  is a flowchart representation of a method of delivering feedback coordinated with a virtual interaction 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 predictive quantization of user interaction with a virtual musical instrument in computer-generated reality (CGR) environments. According to some implementations, the method is performed by a device with one or more processors, non-transitory memory, and one or more user interaction hardware components configured to enable a user to play a virtual instrument in a CGR environment. The method also includes: obtaining user movement information, wherein the user movement information characterizes real-world body pose and trajectory information of the user; generating, from real-world user movement information and a predetermined placement of the virtual instrument in the CGR environment, a predicted virtual instrument interaction time for a virtual instrument interaction prior to the virtual instrument interaction occurring; determining whether or not the predicted virtual instrument interaction time falls within an acceptable temporal range around one of a plurality of temporal sound markers; and in response to determining that the predicted virtual instrument interaction time falls within the acceptable temporal range around a particular temporal sound marker of the plurality of temporal sound markers, quantizing the virtual instrument interaction by presenting play of the virtual instrument to match the particular temporal sound marker of the plurality of temporal sound markers. 
     Various implementations disclosed herein include devices, systems, and methods for generating a predicted virtual interaction time from user movement information prior to a virtual interaction in order to deliver feedback (e.g., sound, haptics, etc.) coordinated with the virtual interaction. According to some implementations, the method is performed by a device with one or more processors, non-transitory memory, one or more user feedback devices, and one or more input devices configured to enable a user to interact with a CGR item in a CGR environment. The method also includes: obtaining user movement information characterizing real-world body pose and trajectory information of the user; generating a predicted virtual interaction time for a virtual interaction based at least in part on a placement of the CGR item in the CGR environment and the user movement information prior to the virtual interaction occurring; determining a first initiation time for a first feedback device among the one or more feedback devices based at least in part on the predicted virtual interaction time and a first predetermined latency period associated with the first feedback device; and initiating at the first initiation time, by the device, first feedback from the first feedback device in order to satisfy a performance criterion that corresponds to the virtual interaction with the CGR item 
     In accordance with some implementations, a device includes 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 processors, a non-transitory memory, and means for performing or causing performance 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 systems. Physical environments, such as a physical park, include physical articles, such as 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, a computer-generated reality (CGR) environment refers to a wholly or partially simulated environment that people sense and/or interact with via an electronic system. In CGR, 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 CGR environment are adjusted in a manner that comports with at least one law of physics. For example, a CGR system may detect a person&#39;s head turning 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), adjustments to characteristic(s) of virtual object(s) in a CGR environment may be made in response to representations of physical motions (e.g., vocal commands). 
     A person may sense and/or interact with a CGR object using any one of their senses, including sight, sound, touch, taste, and smell. For example, a person may sense and/or interact with audio objects that create 3D or spatial audio environment that provides the perception of point audio sources in 3D space. In another example, audio objects may enable audio transparency, which selectively incorporates ambient sounds from the physical environment with or without computer-generated audio. In some CGR environments, a person may sense and/or interact only with audio objects. 
     A virtual reality (VR) environment refers to a simulated environment that is designed to be based entirely on computer-generated sensory inputs for one or more senses. A VR environment comprises a plurality of virtual objects with which a person may sense and/or interact. For example, computer-generated imagery of trees, buildings, and avatars representing people are examples of virtual objects. A person may sense and/or interact with virtual objects in the VR environment through a simulation of the person&#39;s presence within the computer-generated environment, and/or through a simulation of a subset of the person&#39;s physical movements within the computer-generated environment. 
     In contrast to a VR environment, which is designed to be based entirely on computer-generated sensory inputs, a mixed reality (MR) environment refers to a simulated environment that is designed to incorporate sensory inputs from the physical environment, or a representation thereof, in addition to including computer-generated sensory inputs (e.g., virtual objects). On a virtuality continuum, a mixed reality environment is anywhere between, but not including, a wholly physical environment at one end and virtual reality environment at the other end. 
     In some MR environments, computer-generated sensory inputs may respond to changes in sensory inputs from the physical environment. Also, some electronic systems for presenting an MR environment may track location and/or orientation with respect to the physical environment to enable virtual objects to interact with real objects (that is, physical articles from the physical environment or representations thereof). For example, a system may account for movements so that a virtual tree appears stationery with respect to the physical ground. 
     An augmented reality (AR) environment refers to a simulated environment in which one or more virtual objects are superimposed over a physical environment, or a representation thereof. For example, an electronic system for presenting an AR environment may have a transparent or translucent display through which a person may directly view the physical environment. The system may be configured to present virtual objects on the transparent or translucent display, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. Alternatively, a system may have an opaque display and one or more imaging sensors that capture images or video of the physical environment, which are representations of the physical environment. The system composites the images or video with virtual objects, and presents the composition on the opaque display. A person, using the system, indirectly views the physical environment by way of the images or video of the physical environment, and perceives the virtual objects superimposed over the physical environment. As used herein, a video of the physical environment shown on an opaque display is called “pass-through video,” meaning a system uses one or more image sensor(s) to capture images of the physical environment, and uses those images in presenting the AR environment on the opaque display. Further alternatively, a system may have a projection system that projects virtual objects into the physical environment, for example, as a hologram or on a physical surface, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. 
     An augmented reality environment also refers to a simulated environment in which a representation of a physical environment is transformed by computer-generated sensory information. For example, in providing pass-through video, a system may transform one or more sensor images to impose a select perspective (e.g., viewpoint) different than the perspective captured by the imaging sensors. As another example, a representation of a physical environment may be transformed by graphically modifying (e.g., enlarging) portions thereof, such that the modified portion may be representative but not photorealistic versions of the originally captured images. As a further example, a representation of a physical environment may be transformed by graphically eliminating or obfuscating portions thereof. 
     An augmented virtuality (AV) environment refers to a simulated environment in which a virtual or computer-generated environment incorporates one or more sensory inputs from the physical environment. The sensory inputs may be representations of one or more characteristics of the physical environment. For example, an AV park may have virtual trees and virtual buildings, but people with faces photorealistically reproduced from images taken of physical people. As another example, a virtual object may adopt a shape or color of a physical article imaged by one or more imaging sensors. As a further example, a virtual object may adopt shadows consistent with the position of the sun in the physical environment. 
     There are many different types of electronic systems that enable a person to sense and/or interact with various CGR environments. Examples include head mounted 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 mounted system may have one or more speaker(s) and an integrated opaque display. Alternatively, a head mounted system may be configured to accept an external opaque display (e.g., a smartphone). The head mounted 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 mounted 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, μLEDs, 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 one embodiment, 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. 
     Some existing CGR systems enable a user to play a virtual musical instrument by translating the real-world body pose of the user and trajectory information (hereinafter “user movement information”) into CGR interactions between a CGR avatar/representation associated with the user and the virtual instrument. For example, by translating user movement information, a CGR system presents user play of the virtual instrument, which is provided as CGR feedback (e.g., CGR audio, video, haptics, etc.) in the CGR environment (e.g., virtual collisions of a virtual drumstick with a virtual drum). 
     For example, a user may not move in synchronization with respect to a piece of music characterized by a plurality of temporal sound markers. As such, a direct translation of user movement information may result in virtual instrument interactions (e.g., virtual collisions on a virtual drum face) that are not in synchronization with one or more of the plurality of temporal sound markers. Various implementations of the present invention enable CGR systems to generate a predicted virtual instrument interaction time from real-world user movement information prior to the virtual instrument interaction occurring. The system determines whether or not the predicted virtual instrument interaction time falls within an acceptable temporal range around one of the plurality of temporal sound markers and quantizes the virtual instrument interaction by presenting play of the virtual instrument to match one of the plurality of temporal sound markers. 
     As noted above, a feedback device (such as speaker/headphones, haptics engine, or the like) often has a predetermined latency between initiation of associated feedback (such as audio, haptics, or the like) and user perception of the associated feedback due to, for example, hardware and/or transmission delays. In turn, feedback associated with a virtual interaction in a CGR environment should ideally strive for life-like coordination (or synchronization) between perception of the feedback and occurrence of the virtual interaction itself. However, existing CGR delivery systems continue to face challenges when it comes to effective and timely coordination (or synchronization) between the feedback and the virtual interaction itself. Various implementations of the present invention enable CGR systems to generate a predicted virtual interaction time from user movement information prior to the virtual interaction in order to deliver feedback (e.g., sound, haptics, etc.) coordinated with the virtual interaction. 
       FIG. 1  is a block diagram of an example operating environment  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 environment  100  includes a controller  110 , a head-mounted device (HMD)  120 , hand-held devices  130 A and  130 B, and optional motion capture devices  170 A and  170 B. While the exemplary operating environment  100  in  FIG. 1  includes two hand-held devices  130 A and  130 B, those of ordinary skill in the art will appreciate from the present disclosure that the operating environment of various implementations of present invention may include any number of hand-held devices, such as one hand-held device. While the exemplary operating environment  100  in  FIG. 1  includes two optional motion capture devices  170 A and  170 B, those of ordinary skill in the art will appreciate from the present disclosure that the operating environment of various implementations of present invention may include any number of motion capture devices, such as a single motion capture device. 
     In some implementations, the controller  110  is configured to manage and coordinate a CGR experience for a user  150 . 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 scene  105 . For example, the controller  110  may be a local server situated within the scene  105 . In another example, the controller  110  is a remote server situated outside of the scene  105  (e.g., a cloud server, central server, etc.). 
     In some implementations, the controller  110  is communicatively coupled with the hand-held devices  130 A and  130 B via wired or wireless communication channels  140 A and  140 B (e.g., BLUETOOTH, Institute of Electrical and Electronics Engineers (IEEE) 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). In some implementations, the controller  110  is communicatively coupled with the HMD  120  via a wired or wireless communication channel  144  (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). In some implementations, the controller  110  is communicatively coupled with the motion capture devices  170 A and  170 B via wired or wireless communication channels  172 A and  172 B (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). In some implementations, the HMD  120  is communicatively coupled with the hand-held devices  130 A and  130 B and/or the motion capture devices  170 A and  170 B via wired or wireless communication channels (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.) (not shown). 
     In some implementations, the HMD  120  is configured to present the CGR experience to the user  150 . In some implementations, the HMD  120  includes a suitable combination of software, firmware, and/or hardware. The HMD  120  is described in greater detail below with respect to  FIG. 3 . In some implementations, the functionalities of the controller  110  are provided by and/or combined with the HMD  120 . 
     According to some implementations, the HMD  120  presents a CGR experience  124  to the user  150  while the user  150  is virtually and/or physically present within the scene  105 . In some implementations, while presenting an augmented reality (AR) experience, the HMD  120  is configured to present AR content and to enable optical see-through of the scene  105 . For example, the CGR experience  124  corresponds to a composite of the AR content and the optical see-through of the scene  105 . In some implementations, while presenting a virtual reality (VR) or mixed reality (MR) experience, the HMD  120  is configured to present VR or MR content and to enable video pass-through of the scene  105 . For example, the CGR experience  124  corresponds to a composite of the VR or MR content and the video pass-through of the scene  105 . 
     In some implementations, the user  150  wears the HMD  120  on his/her head. As such, the HMD  120  includes one or more displays provided to display CGR content. For example, the HMD  120  encloses the field-of-view of the user  150 . For example, the user  150  wears a head-mounted enclosure on his/her head that is capable of receiving or otherwise attaching a mobile device (e.g., a phone, tablet, or the like), wherein the combination thereof functions as the HMD  120 . In some implementations, the HMD  120  is replaced with a CGR chamber, enclosure, or room configured to present CGR content in which the user  150  does not wear the HMD  120 . 
     In some implementations, the hand-held devices  130 A and  130 B provide input data to the controller  110  and/or the HMD  120  while the user  150  is virtually and/or physically within the scene  105 . In some implementations, the hand-held devices  130 A and  130 B provide data describing one or more selections made by the user  150  via interaction hardware mechanisms (e.g., buttons or the like on the hand-held devices  130 A and  130 B) to the controller  110  and/or the HMD  120 . In some implementations, the hand-held devices  130 A and  130 B provide data describing a position and/or an orientation of the hand-held devices  130 A and  130 B within the scene  105  to the controller  110  and/or the HMD  120 . In some implementations, the controller  110  and/or the HMD  120  may use such position and/or orientation data to estimate a position and/or orientation of the user  150  within the scene  105 . In some implementations, the hand-held devices  130 A and  130 B provide data describing one or more physiological conditions of the user  150  to the controller  110  and/or the HMD  120 . For example, the hand-held devices  130 A and  130 B may provide data describing a heart rate of the user  150 , a blood pressure of the user  150 , and/or the like to the controller  110  and/or the HMD  120 . A hand-held device, such as the hand-held devices  130 A or  130 B, is described in greater detail below with respect to  FIG. 4 . 
     In some implementations, the optional motion capture devices  170 A and  170 B correspond to fixed or movable sensory equipment within the scene  105  (e.g., image sensors, depth sensors, infrared (IR) sensors, event cameras, etc.). In some implementations, each of the motion capture devices  170 A and  170 B is configured to provide movement information to the controller  110  and/or the HMD  120  while the user  150  is physically within the scene  105 . In some implementations, the motion capture devices  170 A and  170 B include image sensors (e.g., cameras), and the movement information includes images of the user  150 . In some implementations, the movement information characterizes body poses of the user  150  at different times. In some implementations, the movement information characterizes head poses of the user  150  at different times. In some implementations, the movement information characterizes hand tracking information associated with the hands of the user  150  at different times. In some implementations, the movement information characterizes the velocity and/or acceleration of body parts of the user  150  such as his/her hands. In some implementations, the movement information indicates joint positions and/or joint orientations of the user  150 . In some implementations, the motion capture devices  170 A and  170 B include feedback devices such as speakers, lights, or the like. 
     In some implementations, the HMD  120  presents a CGR experience  124  to the user  150 . In some implementations, the HMD  120  includes one or more displays (e.g., a single display or one for each eye). In such implementations, the HMD  120  presents the CGR experience  124  by displaying data corresponding to the CGR experience  124  on the one or more displays or by projecting data corresponding to the CGR experience  124  onto the retinas of the user  150 . In the example of  FIG. 1 , the CGR experience  124  includes a CGR representation  126  of the user  150 . In some implementations, the controller  110  and/or the HMD  120  cause the CGR representation  126  to move based the movement information from the HMD  120 , the hand-held devices  130 A and  130 B, and/or the motion capture devices  170 A and  170 B. 
       FIG. 2  is a block diagram 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 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 including an optional operating system  230  and a CGR experience module  240 . 
     The operating system  230  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR experience module  240  is configured to manage and coordinate one or more CGR experiences for one or more users (e.g., a single CGR experience for one or more users, or multiple CGR experiences for respective groups of one or more users). To that end, in various implementations, the CGR experience module  240  includes a data obtaining unit  242 , a tracking unit  244 , a prediction unit  245 , a coordination unit  246 , and a data transmitting unit  248 . 
     In some implementations, the data obtaining unit  242  is configured to obtain data (e.g., presentation data, interaction data, sensor data such as user movement information, location data, etc.) from at least one of the HMD  120 , the hand-held devices  130 A and  130 B, and/or the optional motion capture devices  170 A and  170 B. To that end, in various implementations, the data obtaining unit  242  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the tracking unit  244  is configured to map the scene  105  and to track the position/location of at least one of the HMD  120  and the hand-held devices  130 A and  130 B with respect to the scene  105 . To that end, in various implementations, the tracking unit  244  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the prediction unit  245  is configured to generate a predicted virtual interaction time for a virtual interaction based at least in part on a placement of a CGR item in a CGR environment and the user movement information prior to the virtual interaction occurring. To that end, in various implementations, the prediction unit  245  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the coordination unit  246  is configured to manage and coordinate the CGR experience presented to the user  150  by the HMD  120 . In some implementations, the coordination unit  246  is also configured to coordinate initiation of feedback associated with the CGR experience based at least in part on the predicted virtual interaction time. In some implementations, the coordination unit  246  sends feedback signals and timing information to the HMD  120 , the hand-held devices  130 A and  130 B, and/or the motion capture devices  170 A and  170 B in order to coordinate initiation of feedback associated with the CGR experience. To that end, in various implementations, the coordination unit  246  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  248  is configured to transmit data (e.g., presentation data, feedback signals and timing information, location data, etc.) to at least one of the HMD  120 , the hand-held devices  130 A and  130 B, and the motion capture devices  170 A and  170 B. To that end, in various implementations, the data transmitting unit  248  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  242 , the tracking unit  244 , the prediction unit  245 , the coordination unit  246 , and the data transmitting unit  248  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 obtaining unit  242 , the tracking unit  244 , the prediction unit  245 , the coordination unit  246 , and the data transmitting unit  248  may be located in separate computing devices. 
     Moreover,  FIG. 2  is intended more as functional description of the various features which be present in a particular embodiment 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 embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG. 3  is a block diagram of the head-mounted device (HMD)  120  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 HMD  120  includes one or more processing units  302  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more 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 , 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 thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oxygen sensor, blood glucose sensor, etc.), one or more microphones, one or more speakers, a haptics engine, one or more image sensors (e.g., including one or more external-facing images sensors and/or one or more internal-facing image sensors), gaze tracker, and/or the like. 
     In some implementations, the one or more displays  312  are configured to present the CGR experience to the user  150 . 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 HMD  120  may include a single display. In another example, the HMD  120  may include a display for each eye of the user  150 . In some implementations, the one or more displays  312  are capable of presenting AR, MR, and VR content. In some implementations, the one or more displays  312  are capable of presenting AR, MR, or VR content. 
     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 CGR presentation module  340 . 
     The operating system  330  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR presentation module  340  is configured to present CGR content to the user  150  via the one or more displays  312 . To that end, in various implementations, the CGR presentation module  340  includes a data obtaining unit  342 , a CGR presenting unit  344 , a feedback unit  346 , and a data transmitting unit  350 . 
     In some implementations, the data obtaining unit  342  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, etc.) from the controller  110 , the hand-held devices  130 A and  130 B, and/or the optional motion capture devices  170 A and  170 B. To that end, in various implementations, the data obtaining unit  342  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the CGR presenting unit  344  is configured to present CGR content and/or to initiate visual initiate feedback associated with the CGR content via the one or more displays  312 . To that end, in various implementations, the CGR presenting unit  344  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the feedback unit  346  is configured to initiate feedback associated with the CGR experience via the one or more I/O devices and sensors  306  (e.g., the haptics engine, the speakers, etc.). To that end, in various implementations, the feedback unit  346  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  350  is configured to transmit data (e.g., presentation data, sensor data such as user movement information, location data, etc.) to at least one of the controller  110  and the hand-held devices  130 A and  130 B. To that end, in various implementations, the data transmitting unit  350  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  342 , the CGR presenting unit  344 , the feedback unit  346 , and the data transmitting unit  350  are shown as residing on a single device (e.g., the HMD  120 ), it should be understood that in other implementations, any combination of the data obtaining unit  342 , the CGR presenting unit  344 , the feedback unit  346 , and the data transmitting unit  350  may be located in separate computing devices. 
     Moreover,  FIG. 3  is intended more as functional description of the various features which be present in a particular embodiment 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 embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG. 4  is a block diagram of a hand-held device  400  (e.g., one of the hand-held devices  130 A and  130 B in  FIG. 1 ) 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 hand-held device  400  includes one or more processing units  402  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more I/O devices and sensors  406 , one or more communication interfaces  408  (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  410 , a memory  420 , and one or more communication buses  404  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  404  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  406  include at least one of one or more buttons or interaction hardware mechanisms, one or more IMUs, an accelerometer, a gyroscope, a torque meter, a force meter, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oxygen sensor, blood glucose sensor, etc.), a haptics engine, a keyboard, a mouse, a touchpad, 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  420  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  420  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  420  optionally includes one or more storage devices remotely located from the one or more processing units  402 . The memory  420  comprises a non-transitory computer readable storage medium. In some implementations, the memory  420  or the non-transitory computer readable storage medium of the memory  420  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  430  and a CGR experience module  440 . 
     The operating system  430  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR experience module  440  is configured to receive input data from a user  150  and generate data corresponding to a CGR experience for the user  150 . For example, the CGR experience module  440  is configured to enable the user  150  to play a virtual musical instrument by translating movement information associated with the user  150  into interactions between the CGR avatar/representation of the user  150  and the virtual instrument. To that end, in various implementations, the CGR experience module  440  includes a data obtaining unit  442 , a feedback unit  446 , and a data transmitting unit  448 . 
     In some implementations, the data obtaining unit  442  is configured to obtain data (e.g., presentation data, interaction data, sensor data such as user movement information, location data, etc.) from at least one of the controller  110  and the HMD  120 . In some implementations, the data obtaining unit  442  is configured to obtain sensor data from the I/O devices and sensors  406  such as velocity, acceleration, force of impact, and/or the like associated with the hand-held device  400 . To that end, in various implementations, the data obtaining unit  442  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the feedback unit  446  is configured to initiate feedback associated with the CGR experience via the one or more I/O devices and sensors  406  (e.g., the haptics engine, the speakers, etc.). To that end, in various implementations, the feedback unit  446  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  448  is configured to transmit data (e.g., presentation data, sensor data such as user movement information, location data, etc.) to at least one of the controller  110  and the HMD  120 . To that end, in various implementations, the data transmitting unit  448  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  442 , the feedback unit  446 , and the data transmitting unit  448  are shown as residing on a single device (e.g., the hand-held device  400 ), it should be understood that in other implementations, any combination of the data obtaining unit  442 , the feedback unit  446 , and the data transmitting unit  448  may be located in separate computing devices. 
     Moreover,  FIG. 4  is intended more as functional description of the various features which be present in a particular embodiment 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. 4  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 embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIGS. 5A and 5B  illustrate example virtual instrument interaction data  500 / 500 Q in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example,  FIG. 5A  illustrates example virtual instrument interaction data  500  before quantization, while  FIG. 5B  illustrates example virtual instrument interaction data  500 Q after quantization. 
     As illustrated in  FIGS. 5A and 5B , the virtual instrument interaction data  500 / 500 Q include temporal sound markers  510 A- 510 C. Each temporal sound marker indicates a particular point in time (e.g., an absolute point in time or a point in time defined relative to another point in time, such as a point in time at which a sequence of virtual musical instrument interactions start). In some implementations, the temporal sound markers  510 A- 510 C may define a time space within which virtual instrument interactions occur. 
     Each the temporal sound markers  510 A- 510 C is associated with an acceptable temporal range  520 A- 520 C, a portion of the time space between thresholds around the temporal sound marker in  FIGS. 5A and 5B . For example, a temporal sound marker  510 A is associated with the acceptable temporal range  520 A bounded by thresholds  512 A and  512 B, a temporal sound marker  510 B is associated with the acceptable temporal range  520 B bounded by thresholds  514 A and  514 B, and a temporal sound marker  520 C is associated with the acceptable temporal range  520 C bounded by thresholds  516 A and  516 B. While the virtual instrument interaction data  500 / 500 Q illustrated in  FIGS. 5A and 5B  depict the acceptable temporal ranges  520 A- 520 C of the various temporal sound markers  510 A- 510 C as being of substantially equal size, those of ordinary skill in the art will appreciate from the present disclosure that each of the temporal sound markers may have an acceptable temporal range that is substantially different in size than the acceptable temporal ranges of other temporal sound markers. 
     As illustrated in  FIGS. 5A and 5B , the virtual instrument interaction data  500 / 500 Q also include virtual instrument interactions  530 ,  532 ,  532 Q, and  534 . Each virtual instrument interaction may describe one or more aspects of a user interaction with a virtual musical instrument. Therefore, a virtual instrument interaction may be associated with one or more properties, such as a predicted pitch of the virtual instrument interaction, a predicted sound intensity of the virtual instrument interaction, a predicted virtual instrument interaction time of the virtual instrument interaction, a predicted note of the virtual instrument interaction, and/or the like. The predicted virtual instrument interaction time of a virtual instrument interaction may indicate a predicted time at which user will experience a virtual collision with a virtual instrument given the real-world body pose and trajectory of the user. In the example virtual instrument interaction data  500  illustrated in  FIG. 5A , the predicted virtual instrument interaction time of a virtual instrument interaction is represented by the horizontal location of the virtual instrument interactions  530 ,  532 , and  534  within the virtual instrument interaction data  500 . For example, the virtual instrument interaction  530  occurs at a predicted virtual instrument interaction time associated with the temporal sound marker  510 A in both  FIG. 5A  and  FIG. 5B . 
     In some implementations, if a predicted virtual instrument interaction time of a virtual instrument interaction falls within an acceptable temporal range of a temporal sound marker, a device (e.g., the controller  110  in  FIGS. 1-2 ) quantizes the virtual instrument interaction by presenting play of the virtual instrument interaction at the temporal sound marker. For example,  FIG. 5A  illustrates that the predicted virtual instrument interaction time for the virtual instrument interaction  532  is prior to temporal sound marker  510 B but within the acceptable temporal range  520 B of the temporal sound marker  510 B. In response, the device (e.g., the controller  110  in  FIGS. 1-2 ) does not cause presentation of play of virtual instrument interaction  532 . Instead, the device (e.g., the controller  110  in  FIGS. 1-2 ) quantizes the virtual instrument interaction  532  by causing presentation of play of the virtual instrument interaction  532 Q at the temporal sound marker  510 B, as illustrated in  FIG. 5B . In contrast, the predicted virtual instrument interaction time for virtual instrument interaction  534  is outside the acceptable temporal range  520 C of temporal sound marker  510 C. Thus, the device (e.g., the controller  110  in  FIGS. 1-2 ) does not cause presentation of play of the virtual instrument interaction  534  after the temporal sound marker  510 C. 
       FIG. 6  is a flowchart representation of a method  600  of predictive quantization of a virtual instrument interaction in accordance with some implementations. In various implementations, the method  600  is performed by a device (e.g., the controller  110  in  FIG. 1 , the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , the and/or a suitable combination thereof) with one or more processors, non-transitory memory, and one or more user interaction hardware components configured to enable a user to play a virtual instrument in a computer-generated reality (CGR) environment. In some implementations, the method  600  is performed by processing logic, including hardware, firmware, software, or a suitable combination thereof. In some implementations, the method  600  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  6 - 1 , the method  600  includes obtaining user movement information. In some implementations, the user movement information includes data characterizing a real-world pose and trajectory of the user. In some implementations, the user movement information includes information characterizing a velocity, an acceleration, a force of impact, and/or the like of user interaction with at least one user interaction hardware component (e.g., the hand-held devices  130 A and  130 B in  FIG. 1 ). In some implementations, the user movement information includes information characterizing a direction of the gaze of the user. 
     In some implementations, the user movement information includes data determined using images captured by image sensors (e.g., within the HMD  120  in  FIG. 1 , and/or within the hand-held devices  130 A and  130 B in  FIG. 1 ) and/or data determined using output of IMUs, gyroscopes, accelerometers, torque meter, force meters, and/or other sensors (e.g., within the HMD  120  in  FIG. 1 , and/or within the hand-held devices  130 A and  130 B in  FIG. 1 ). For example, the user movement information includes data characterizing the force of impact, angle of impact, and position of impact of the user movement relative to virtual instrument (e.g., drum head, keyboard key, guitar string, or the like) from the IMUs, gyroscopes, accelerometers, torque meter, force meters, and/or other sensors of the hand-held devices  130 A and  130 B in  FIG. 1 . In this example, the device uses the motion and predicted collision to determine characteristics of the note to be played. As such, for example, if the user hits a virtual cymbal at high speed, the device plays a louder note based on the predicted speed or force of impact. Or, as another example, if the device predicts that the user will hit a certain portion of the virtual cymbal or that the user&#39;s finger will hit a particular key on a virtual piano, the device plays a different sound or plays a different note based on the predicted position or angle of impact. 
     As another example, the user movement information includes data determined by extracting image features within images captured by image sensors and determining the real-world pose and trajectory of the user based on the extracted image features. The image sensors configured to capture the images may include one or more of: (i) a forward-facing camera configured to capture images from a point of view characterized by an axis substantially parallel to an axis characterizing a point of view of the user; and/or (ii) a non-forward-facing camera (e.g., a downward-facing camera) configured to capture images from a point of view characterized by an axis substantially perpendicular to an axis characterizing a point of view of the user. 
     In some implementations, the device is configured to translate the user movement information into a virtual instrument interaction. In some implementations, translating the user movement information into a virtual instrument interaction includes determining whether real-world body pose and trajectory of the user falls within a predicted placement of the virtual instrument within a CGR environment, e.g., a three-dimensional region associated with a particular virtual instrument. In some implementations, translating the user movement information into a virtual instrument interaction includes determining whether real-world body pose and trajectory of the user falls within a three-dimensional region associated with a particular virtual instrument interaction, e.g., a three-dimensional region associated with a particular virtual instrument interaction associated with a region of a virtual drum or a key of a virtual keyboard. 
     As represented by block  6 - 2 , the method  600  includes generating a predicted virtual instrument interaction time for a virtual instrument interaction based at least in part on the user movement information prior to occurrence of the virtual instrument interaction. In some implementations, the device generates the predicted virtual musical instrument time from the user movement information and a predetermined placement of the virtual instrument in the CGR environment. For example, the device: (1) obtains coordinates of a predicted placement of the virtual instrument in the CGR environment; and (2) predicts, given the real-world body pose and the trajectory of the user determined from the user movement information, whether and when the movement pattern of the user will intersect with the predicted placement of the virtual instrument. 
     As represented by block  6 - 3 , the method  600  includes determining whether the predicted virtual instrument interaction time for the virtual instrument interaction falls within an acceptable temporal range of a temporal sound marker. In some implementations, the device determines an acceptable temporal range for each sound marker and then determines whether the predicted virtual instrument interaction time falls within the acceptable range. In some implementations, the device determines the acceptable temporal range for a sound marker based on data characterizing past user interaction with the device. For example, data characterizing past user interaction with the device may indicate that a user typically has a particular degree of temporal imprecision in his/her interactions with a virtual musical instrument. Thus, in some implementations, the device may determine the acceptable temporal range for a temporal sound marker based on the particular degree of temporal imprecision. 
     As represented by block  6 - 4 , the method  600  includes quantizing virtual instrument interaction in response to determining that the predicted virtual instrument interaction time for the virtual instrument interaction falls within the acceptable temporal range of a temporal sound marker. In some implementations, in response to determining that the predicted virtual instrument interaction time for the virtual instrument interaction falls within the acceptable temporal range of a particular temporal sound marker, the device presents play of the virtual instrument interaction to match the particular temporal sound marker. 
     In some implementations, presenting play of the virtual instrument comprises producing one or more CGR feedbacks, such as an audio feedback, a video feedback, a haptic feedback, and/or the like. The device may modify one or more of those CGR feedbacks in response to quantizing virtual instrument interaction. For example, the device may cause an earlier display of virtual collision and/or an earlier generation of haptic feedback. 
       FIG. 7  is a flowchart representation of a method  700  of presenting user play of a virtual musical instrument in accordance with some implementations. In various implementations, the method  700  is performed by a device (e.g., the controller  110  in  FIG. 1 , the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , the and/or a suitable combination thereof) with one or more processors, non-transitory memory, and one or more user interaction hardware components configured to enable a user to play a virtual instrument in a computer-generated reality (CGR) environment. In some implementations, the method  700  is performed by processing logic, including hardware, firmware, software, or a suitable 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). 
     As represented by block  7 - 11 , the method  700  includes obtaining sensor data. In some implementations, the sensor data includes data obtained from one or more sensors (e.g., associated with the HMD  120  in  FIG. 1 , and/or associated with the hand-held devices  130 A and  130 B in  FIG. 1 ). Examples of sensors include image sensors and IMUs. 
     As represented by block  7 - 12 , the method  700  includes processing the sensor data to determine the body pose and trajectory of the user. For example, the device may determine real-world body pose and trajectory of the user based on features in one or more images captured by one or more image sensors. In some implementations, the device may supply information determined using images and information determined using IMUs as inputs to a machine learning model configured to determine real-world body pose and trajectory based on the inputs. 
     As represented by block  7 - 21 , the method  700  includes obtaining three-dimensional regions (e.g., in a CGR environment) corresponding to a particular virtual instrument interaction with the virtual instrument. For example, the device may obtain coordinates that define placement of a region of a virtual drum or a key of a virtual keyboard within the CGR environment. 
     As represented by block  7 - 22 , the method  700  includes determining whether the body pose and trajectory of the user fall within one of the three-dimensional regions associated with a particular virtual instrument interaction with the virtual instrument. For example, the device determines whether the real-world body pose and trajectory falls within the three-dimensional region for placement of a region of a virtual drum or a key of a virtual keyboard. 
     If the device determines that the body pose and trajectory of the user do not fall within the three-dimensional region for any virtual instrument interactions (the “NO” branch from block  7 - 22 ) the method  700  includes not translating the body pose and trajectory to a virtual instrument interaction as represented by block  7 - 23 . If the device determines that the body pose and trajectory fall within one of the three-dimensional regions associated with a particular virtual instrument interaction (the “YES” branch from block  7 - 22 ), method  700  includes translating the body pose and trajectory of the user to the particular virtual instrument interaction. 
     According to some implementations, as represented by blocks  7 - 31  through  7 - 37 , the method  700  includes quantizing the virtual instrument interaction. As represented by block  7 - 31 , the method  700  includes obtaining a predicted placement (e.g., three-dimensional coordinates) of a virtual instrument within the CGR environment. As represented by block  7 - 32 , the methods  700  includes determining a predicted virtual instrument interaction time for the virtual instrument interaction based on both: (i) the body pose and trajectory of the user; and (ii) the predicted placement of the virtual instrument. As represented by block  7 - 33 , the method  700  includes identifying one or more temporal sound markers. As represented by block  7 - 34 , the method  700  also includes determining an acceptable temporal range for each sound marker. As represented by block  7 - 35 , the method  700  includes determining whether the predicted virtual instrument interaction time for the virtual instrument interaction falls within the acceptable temporal range of a particular temporal sound marker. 
     If the predicted virtual instrument interaction time for the virtual instrument interaction does not fall within the acceptable temporal range of a particular temporal sound marker (the “NO” branch from block  7 - 35 ), the method  700  includes not associating the virtual instrument interaction with the particular temporal sound marker as represented by block  7 - 36 . If the predicted virtual instrument interaction time for the virtual instrument interaction falls within the acceptable temporal range of a particular temporal sound marker (the “YES” branch from block  7 - 35 ), the method  700  includes associating the virtual instrument interaction with the particular temporal sound marker as represented by block  7 - 37 . 
     As represented by block  7 - 41 , the method  700  includes producing one or more CGR feedbacks (e.g., audio feedback, haptic feedback, and/or the like) corresponding to the virtual instrument interaction. In some implementations, a CGR feedback may be any output of a device associated with a CGR system (e.g., an output from the HMD  120  in  FIGS. 1 and 3 , or an output from the hand-held devices  130 A and  130 B in  FIG. 1 ). Examples of CGR feedbacks include audio feedbacks, video feedbacks, and haptic feedbacks. The device may modify one or more of those CGR feedbacks in response to quantizing virtual instrument interaction. 
     In some implementations, if the predicted virtual instrument interaction time for the virtual instrument interaction does not fall within the acceptable temporal range of a particular temporal sound marker (the “NO” branch from block  7 - 35 ), the method  700  includes, producing one or more CGR feedbacks (e.g., audio feedback, haptic feedback, and/or the like) at the actual impact time (or latency compensated impact time as described below with respect to the method  800 ) rather than at the quantized time. 
       FIG. 8  is a flowchart representation of a method  800  of delivering feedback coordinated with a virtual interaction in accordance with some implementations. In various implementations, the method  800  is performed by a device (e.g., the controller  110  in  FIG. 1 , the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , and/or a suitable combination thereof) with one or more processors, non-transitory memory, one or more feedback devices, and one or more input devices configured to enable a user to interact with a computer-generated reality (CGR) item in a CGR environment. In some implementations, the method  800  is performed by processing logic, including hardware, firmware, software, or a suitable combination thereof. In some implementations, the method  800  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     In some implementations, the device corresponds to the controller  110  in  FIGS. 1 and 2 . In some implementations, the device corresponds to a hand-held electronic device (e.g., a mobile phone, tablet, laptop, wearable computing device, or the like) and the CGR environment corresponds to a composite of video pass-through or optical see-through of a physical environment with CGR content, including the CGR item. In some implementations, the device corresponds to a head-mounted device (e.g., the HMD  120  in  FIG. 1 ). 
     In some implementations, the one or more feedback devices correspond to a haptics engine, display devices, audio generation devices, and/or the like. For example, the one or more feedback devices are embedded in the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , and/or a suitable combination thereof. In some implementations, the one or more input devices correspond to buttons, joysticks, an IMU, a gyroscope, an accelerometer, image sensors, physiological sensors, grip sensors, a gazer tracker, microphones, and/or the like. For example, the one or more inputs devices are embedded in the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , the motion capture devices  170 A and  170 B in  FIG. 1 , and/or a suitable combination thereof. 
     As represented by block  8 - 1 , the method  800  includes obtaining user movement information characterizing real-world body pose and trajectory information of the user. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the data obtaining unit  242 ) obtains the user movement information from the HMD  120 , the hand-held devices  130 A and  130 B, and/or the optional motion capture devices  170 A and  170 B shown in  FIG. 1 . 
     In some implementations, obtaining the user movement information includes receiving the user movement information from a local and/or remote source. In some implementations, obtaining the user movement information includes retrieving the user movement information from a local and/or remote source. In some implementations, obtaining the user movement information includes generating the user movement information based on locally captured and/or remotely captured sensor data. For example, the device generates the user movement information based on sensor data from image sensors, accelerometers, gyroscopes, depth sensors, IMUs, IR sensors, gaze trackers, and/or the like of the HMD  120 , the hand-held devices  130 A and  130 B, and/or the optional motion capture devices  170 A and  170 B shown in  FIG. 1 . 
     In some implementations, the user movement information includes information characterizing a velocity of a user interaction with at least one of the one or more input devices (e.g., the hand-held devices  130 A and  130 B in  FIG. 1 ). In some implementations, the user movement information includes information characterizing an acceleration of a user interaction with at least one of the one or more input devices (e.g., the hand-held devices  130 A and  130 B in  FIG. 1 ). In some implementations, the user movement information includes information characterizing a direction of user gaze. 
     In some implementations, obtaining the user movement information includes determining the user movement information using one or more image sensors. For example, the image sensors are local to the device (e.g., the image sensors of the HMD  120  in  FIG. 1 ) and/or remote from the device (e.g., the image sensors of the hand-held devices  130 A and  130 B in  FIG. 1  and/or the motion capture devices  170 A and  170 B in  FIG. 1 ). In some implementations, the one or more image sensors include a forward-facing camera, wherein the forward-facing camera is configured to capture images from a point of view characterized by an axis substantially parallel to an axis characterizing a point of view of the user. In some implementations, the one or more image sensors include a non-forward-facing camera, wherein the non-forward-facing camera is configured to capture images from a point of view characterized by an axis substantially perpendicular to an axis characterizing a point of view of the user. For example, the non-forward-facing camera is a downward-facing camera. 
     As represented by block  8 - 2 , the method  800  includes generating a predicted virtual interaction time for a virtual interaction based at least in part on a placement of the CGR item in the CGR environment and the user movement information prior to the virtual interaction occurring. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the prediction unit  245 ) generates a predicted virtual interaction time for a virtual interaction based at least in part on a placement of the CGR item in the CGR environment and the user movement information obtained by the controller  110  or a component thereof (e.g., the data obtaining unit  242 ). 
     In some implementations, the virtual interaction corresponds to a collision with the CGR item. For example, the virtual interaction corresponds to a collision between the CGR item and the user of a CGR item controlled by the user that causes a temporal event such as hitting a CGR drum head with a CGR drum stick, hitting a CGR baseball with a CGR baseball bat, swinging a CGR hammer into a CGR board, striking a CGR punching bag with an avatar&#39;s CGR fists, or the like. 
     In some implementations, the device obtains coordinates for CGR items within the CGR environment. In some implementations, the placement of the CGR item in the CGR environment includes coordinates of at least one surface of the CGR item in the CGR environment. 
     As represented by block  8 - 3 , the method  800  includes determining a first initiation time for a first feedback device among the one or more feedback devices (e.g., an audio generator, haptics engine, or the like) based at least in part on the predicted virtual interaction time and a first predetermined latency period associated with the first feedback device. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines a first initiation time for a first feedback device based at least in part on the predicted virtual interaction time determined by the controller  110  or a component thereof (e.g., the prediction unit  245 ) and a first predetermined latency period associated with the first feedback device. In some implementations, the device obtains predetermined latency periods for the one or more feedback devices from a local source (e.g., a library associated with the functions, latency, historical transmission delays, and/or the like for the feedback devices) and/or a remote source (e.g., the feedback devices themselves, a remote library, and/or the like). 
     In some implementations, determining the first initiation time for the first feedback device includes determining the first initiation time for the first feedback device among the one or more feedback devices (e.g., an audio generator, haptics engine, or the like) based at least in part on the predicted virtual interaction time, the first predetermined latency period associated with the first feedback device, and a perception attribute associated with the first feedback device. For example, the perception attribute corresponds to the physics associated with the modality of the feedback device (e.g., speed of light for a display device versus the speed of sound for an audio generation device). In another example, the perception attribute corresponds to the physiological reaction to and/or perception of feedback associated with the first feedback device by the user. In some implementations, the perception attribute accounts for the speed of sound versus speed of light by adding delay associated with real-world phenomena. As such, for example, audio may be heard by the user after the virtual interaction is perceived visually by the user. 
     In some implementations, determining the first initiation time for the first feedback device includes determining the first initiation time for the first feedback device among the one or more feedback devices (e.g., an audio generator, haptics engine, or the like) based at least in part on the predicted virtual interaction time, the first predetermined latency period associated with the first feedback device, and a delay factor. In some implementations, the delay factor provides for a “delayed reaction” such as a tingling or stinging sensation that occurs Y milliseconds after the virtual interaction. 
     As represented by block  8 - 4 , the method  800  includes initiating at the first initiation time, by the device, first feedback from the first feedback device in order to satisfy a performance criterion that corresponds to the virtual interaction with the CGR item. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) causes initiation of the first feedback, by the first feedback device, at the first initiation time. In some implementations, initiating at the first initiation time, by the device, first feedback from the first feedback device includes, for example, generating an audio signal associated with the first feedback at the first initiation time and transmitting (or providing) the audio signal to an audio generation device (e.g., the first feedback device). 
     In some implementations, the performance criterion corresponds to a synchronization tolerance between the various feedback modalities for the virtual interaction (e.g., X milliseconds tolerance). In some implementations, the performance criterion corresponds to a sensation profile associated with the virtual interaction—different virtual interactions are associated with different modalities of feedback and timing thereof. For example, if a user is swinging a CGR bat to hit a CGR baseball, a baseball striking interaction is associated with a sensation profile that includes: (A) visual feedback associated with the bat impacting the baseball (e.g., a flash of light for an exaggerated indication of the virtual interaction), (B) haptic feedback associated with the bat impacting the baseball, (C) audio feedback associated with the bat impacting the baseball, and (D) delayed haptic feedback associated with a stinging sensation following the bat impacting the baseball. Those of ordinary skill in the art will appreciate from the present disclosure that different virtual interactions can be associated with different sensation profiles in various implementations. 
     In some implementations, the first feedback generated by the first feedback device corresponds to audio feedback. In some implementations, the first predetermined latency period associated with the first feedback device is based at least in part on BLUETOOTH headphones/speakers latency. In some implementations, as will be understood by one of ordinary skill in the art, the first latency predetermined latency period (e.g., audio feedback latency) is based at least in part on one of various wireless communication protocols (e.g., BLUETOOTH, ZIGBEE, NFC, WiFi, LTE, 3G, or the like). For example, with reference to  FIG. 1 , the BLUETOOTH headphones/speakers latency corresponds to: (A) generation of an audio signal by the controller  110 , (B) transmission of the audio signal to the HMD  120  including the BLUETOOTH headphones/speakers (e.g., via a BLUETOOTH communication channel between the controller  110  and the HMD  120 ), and (C) playback of the audio signal by the BLUETOOTH headphones/speakers integrated with the HMD  120 . 
     In some implementations, the first feedback generated by the first feedback device corresponds to haptic feedback. In some implementations, the first predetermined latency period associated with the first feedback device is based at least in part on a motor latency. For example, the motor latency corresponds to: (A) generation of a haptics profile/signal by the controller  110 , (B) transmission of the haptics profile/signal to a haptics engine, and (C) actuation, based on the haptics profile/signal, of a stepper motor, servo motor, linear motor, and/or the like associated with the haptics engine (e.g., the haptics engine is integrated with the HMD  120  and/or the hand-held devices  130 A and  130 B in  FIG. 1 ). 
     In some implementations, the first feedback generated by the first feedback device corresponds to video feedback. In some implementations, the first predetermined latency period associated with the first feedback device is based at least in part on a video pipeline latency. For example, with reference to  FIG. 1 , the video pipeline latency corresponds to: (A) rendering of a frame associated with the CGR experience by the controller  110 , (B) transmission of the frame to the HMD  120 , and (C) display of the frame by a display device of the HMD  120 . 
     In some implementations, the method  800  further includes determining a second initiation time for a second feedback device among the one or more feedback devices (e.g., an audio generator, haptics engine, or the like) based at least in part on the predicted virtual interaction time and a second predetermined latency period associated with the second feedback device. In some implementations, the second initiation time is different from the first initiation time, and wherein a feedback coordination engine coordinates initiating at the first initiation time, by the device, the first feedback from the first feedback device and initiating at the second initiation time, by the device, the second feedback from the second feedback device at the second initiation time in order to satisfy the performance criteria that correspond to the virtual interaction with the CGR item. In some implementations, the first and second feedback devices correspond to different feedback modalities. For example, the first feedback device produces audio feedback, and the second feedback device produces haptic feedback. 
     In some implementations, the method  800  further includes: determining whether or not the predicted virtual interaction time is within an acceptable temporal range around one of a plurality of temporal sound markers; and, in response to determining that the predicted virtual interaction falls within the acceptable temporal range around a particular temporal sound marker of the plurality of temporal sound markers, quantizing the virtual interaction by presenting the virtual interaction to match the particular temporal sound marker of the plurality of temporal sound markers. In some implementations, the CGR item corresponds to a virtual instrument. 
       FIG. 9  is a timing diagram  900  for coordinating feedback from various feedback devices 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 timing diagram  900  is associated with a virtual interaction (e.g., a baseball striking interaction) associated with a sensation profile that includes visual feedback  902 , haptic feedback  904 , audio feedback  906 , and delayed haptic feedback  908 . 
     In some implementations, each virtual interaction is associated with a sensation profile that includes different feedback modalities and timings therefor. For example, if a user is swinging a CGR bat to hit a CGR baseball within a CGR environment, the virtual interaction (e.g., striking the CGR baseball) is associated with a sensation profile that includes: (A) the visual feedback  902  associated with the bat impacting the baseball (e.g., a flash of light for an exaggerated indication of the virtual interaction), (B) the haptic feedback  904  associated with the bat impacting the baseball, (C) the audio feedback  906  associated with the bat impacting the baseball, and (D) the delayed haptic feedback  908  associated with a stinging sensation following the bat impacting the baseball. 
     With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the prediction unit  245 ) determines a predicted virtual interaction time  920  (T 7 ) based on a placement of a CGR item (e.g., the CGR baseball) in a CGR environment and user movement information (e.g., position, velocity, and acceleration of the CGR bat). Generation of a predicted virtual interaction time is described in more detail above with respect to  FIG. 8  and, more specifically, the block  8 - 2  of the method  800 . 
     With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines an initiation time  903  (T 3 ) for the visual feedback  902  associated with a display device based at least in part on the predicted virtual interaction time  920  and a predetermined latency  912  associated with the display device and/or the video delivery pipeline associated therewith. For example, the predetermined latency  912  corresponds to a sum of the time spent: (i) rendering a frame associated with the CGR experience by the controller  110 , (ii) transmitting the frame to the HMD  120 , and (iii) displaying of the frame by the display device of the HMD  120 . 
     With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines an initiation time  905  (T 1 ) for the haptic feedback  904  associated with a haptics engine based at least in part on the predicted virtual interaction time  920  and a predetermined latency  914  associated with the haptics engine and/or the haptics delivery pipeline associated therewith. For example, the predetermined latency  914  corresponds to a sum of the time spent: (i) generating a haptics profile/signal associated with the CGR experience by the controller  110 , (ii) transmitting the haptics profile/signal to the haptics engine (e.g., integrated with the HMD  120  and/or the hand-held devices  130 A and  130 B in  FIG. 1 ), and (iii) actuating, based on the haptics profile/signal, of a stepper motor, servo motor, linear motor, and/or the like associated with the haptics engine. 
     With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines an initiation time  907  (T 2 ) for the audio feedback  906  associated with an audio generation device (e.g., headphones, speakers, or the like) based at least in part on the predicted virtual interaction time  920 , a predetermined latency  916  associated with the audio generation device and/or the audio delivery pipeline associated therewith, and a perception attribute associated with the audio generation device (e.g., the speed of sound). For example, the predetermined latency  916  corresponds to a sum of the time spent: (i) generating an audio signal associated with the CGR experience by the controller  110 , (ii) transmitting the audio signal to the audio generation device (e.g., integrated with the HMD  120  and/or the hand-held devices  130 A and  130 B in  FIG. 1 ), and (iii) playing back the audio signal by the audio generation device. 
     With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines an initiation time  909  (T 3 ) for the delayed haptic feedback  908  associated with the haptics engine based at least in part on the predicted virtual interaction time  920 , the predetermined latency  914  associated with the haptics engine and/or the haptics delivery pipeline associated therewith, and a delay factor. 
     As a result, with continued reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) coordinates feedback for the virtual interaction (e.g., striking the CGR baseball) such that the visual feedback  902  and the haptic feedback  904  occur at the predicted virtual interaction time  920  (T 7 ). With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) coordinates feedback for the virtual interaction such that the audio feedback  906  occurs after the predicted virtual interaction time  920  (at T 8 ) in order to account for the difference between the speed of light and the speed of sound. With reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) coordinates feedback for the virtual interaction such that the delayed haptic feedback  908  occurs after the predicted virtual interaction time  920  (at T 9 ) in order to provide a delayed feedback effect (e.g., a stinging or tingling after-effect). 
       FIG. 10  is a flowchart representation of a method  1000  of delivering feedback coordinated with a virtual interaction in accordance with some implementations. In various implementations, the method  1000  is performed by a device (e.g., the controller  110  in  FIG. 1 , the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , and/or a suitable combination thereof) with one or more processors, non-transitory memory, one or more feedback devices, and one or more input devices configured to enable a user to interact with a computer-generated reality (CGR) item in a CGR environment. In some implementations, the method  1000  is performed by processing logic, including hardware, firmware, software, or a suitable combination thereof. In some implementations, the method  1000  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     In some implementations, the device corresponds to the controller  110  in  FIGS. 1 and 2 . In some implementations, the device corresponds to a hand-held electronic device (e.g., a mobile phone, tablet, laptop, wearable computing device, or the like) and the CGR environment corresponds to a composite of video pass-through or optical see-through of a physical environment with CGR content, including the CGR item. In some implementations, the device corresponds to a head-mounted device (e.g., the HMD  120  in  FIG. 1 ). 
     In some implementations, the one or more feedback devices correspond to a haptics engine, display devices, audio generation devices, and/or the like. For example, the one or more feedback devices are embedded in the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , and/or a suitable combination thereof. In some implementations, the one or more input devices correspond to buttons, joysticks, an IMU, a gyroscope, an accelerometer, image sensors, physiological sensors, grip sensors, a gazer tracker, microphones, and/or the like. For example, the one or more inputs devices are embedded in the HMD  120  in  FIG. 1 , the hand-held devices  130 A and  130 B in  FIG. 1 , the motion capture devices  170 A and  170 B in  FIG. 1 , and/or a suitable combination thereof. 
     As represented by block  10 - 1 , the method  1000  includes obtaining sensor data. In some implementations, the sensor data includes data obtained from one or more sensors (e.g., associated with the HMD  120 , the hand-held devices  130 A and  130 B, and/or the optional motion capture devices  170 A and  170 B in  FIG. 1 ). Examples of the sensors include image sensors, accelerometers, gyroscopes, depth sensors, IMUs, IR sensors, gaze trackers, and/or the like. 
     As represented by block  10 - 2 , the method  1000  includes processing the sensor data to obtain user movement information, including bode pose and trajectory of the user. In some implementations, the device generates the user movement information based on the sensor data. Obtaining the user movement information is described in more detail above with respect to  FIG. 8  and, more specifically, the block  8 - 1  of the method  800 . 
     As represented by block  10 - 3 , the method  1000  includes obtaining placement information for a CGR item. In some implementations, the device obtains coordinates for CGR items within the CGR environment. 
     As represented by block  10 - 4 , the method  1000  includes determining whether or not the user movement information indicates a virtual interaction with the CGR item. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the prediction unit  245 ) determines whether the user movement information indicates the occurrence of a virtual interaction with the CGR item based on the placement information for a CGR item. 
     If the user movement information does not indicate a virtual interaction with the CGR item (the “NO” branch from block  10 - 4 ), the method  1000  continues to block  10 - 5 . If the user movement information indicates a virtual interaction with the CGR item (the “YES” branch from block  10 - 4 ), the method  1000  continues to block  10 - 6 . 
     As represented by block  10 - 5 , the method  1000  includes forgoing generation of a predicted virtual interaction time. 
     As represented by block  10 - 6 , the method  1000  includes generating a predicted virtual interaction time based on the placement of the CGR item and the user movement information. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the prediction unit  245 ) generates a predicted virtual interaction time for a virtual interaction based at least in part on a placement of the CGR item in the CGR environment and the user movement information obtained by the controller  110  or a component thereof (e.g., the data obtaining unit  242 ). Generation of a predicted virtual interaction time is described in more detail above with respect to  FIG. 8  and, more specifically, the block  8 - 2  of the method  800 . 
     As represented by block  10 - 7 A, the method  1000  includes determining initiation time A for feedback device A based on the predicted virtual interaction time and predetermined latency A for the feedback device A. As represented by block  10 - 7 N, the method  1000  includes determining initiation time N for feedback device N based on the predicted virtual interaction time and predetermined latency N for the feedback device N. While the method  1000  includes determining initiation times A and N, those of ordinary skill in the art will appreciate from the present disclosure that various implementations of the method  1000  may include generating any number of initiation times. In some implementations, an initiation time is determined for each feedback device. In some implementations, an initiation time is determined for feedback devices included in a sensation profile associated with the virtual interaction. 
     As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines initiation time A for a feedback device A based at least in part on the predicted virtual interaction time determined by the controller  110  or a component thereof (e.g., the prediction unit  245 ) and a predetermined latency period A associated with the feedback device A. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) determines initiation time N for a feedback device N based at least in part on the predicted virtual interaction time determined by the controller  110  or a component thereof (e.g., the prediction unit  245 ) and a predetermined latency period N associated with the feedback device N. 
     As represented by block  10 - 8 , the method  1000  includes coordinating initiation of feedback A from the feedback device A at the initiation time A with, . . . , initiation of feedback N from the feedback device N at the initiation time N in order to satisfy a performance criterion that corresponds to the virtual interaction with the CGR item. As one example, with reference to  FIGS. 1 and 2 , the controller  110  or a component thereof (e.g., the coordination unit  246 ) causes initiation of feedback A, by feedback device A, at the initiation time A and initiation of feedback N, by feedback device N, at the initiation time N. 
     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 input could be termed a second input, and, similarly, a second input could be termed a first input, which changing the meaning of the description, so long as all occurrences of the “first input” are renamed consistently and all occurrences of the “second input” are renamed consistently. The first input and the second input are both inputs, but they are not the same input. 
     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: 20190924
Publication Date: 20200922
Grant Date: 20200922
Priority Date: 20180927
Inventors: EUBANK, Christopher T.
PATTERSON, DANIEL P.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10H2220/455", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10H2220/401", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10H2220/371", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10H2220/311", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10H1/368", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10H2210/091", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10H1/0008", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/016", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/016", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/016", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 72516811