Abstract: In some implementations, a method is performed by a device including a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, the method includes obtaining user movement information. In some implementations, the user movement information characterizes a first body pose of the user at a first time and a second body pose of the user at a second time. In some implementations, the method includes determining whether a transition from the first body pose to the second body pose satisfies an acceptability threshold. In some implementations, the method includes in response to determining that the transition from the first body pose to the second body pose satisfies the acceptability threshold, generating a sequence of movements for a synthesized reality (SR) representation of the user.

Abstract: In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

Abstract: A method includes determining to present a computer-generated reality (CGR) object that is associated with a first anchor and a second anchor. The method includes determining, based on an image of a physical environment, whether the physical environment includes a portion corresponding to the first anchor. The method includes, in response to determining that the physical environment lacks a portion that corresponds to the first anchor, determining, based on the image, whether the physical environment includes a portion corresponding to the second anchor. The method includes, in response to determining that the physical environment includes a portion that corresponds to the second anchor, displaying, on the display, the CGR object at a location of the display corresponding to the second anchor.

Abstract: In some implementations, a method includes: obtaining a request to display a virtual object at a location within a computer-generated environment; and, in response to detecting the request to display the virtual object the location within the computer-generated environment: determining a real-world pose for the electronic device relative to a first coordinate space associated with a physical environment; determining a viewing vector by transforming the real-world pose from the first coordinate space to a second coordinate space associated with the computer-generated environment; in accordance with a determination that the viewing vector satisfies viewing criteria, displaying the virtual object at the location within the computer-generated environment according to a first view mode; and in accordance with a determination that the viewing vector satisfies second viewing criteria, displaying the virtual object at the location within the computer-generated environment according to a second view mode.

Abstract: In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

Abstract: In one embodiment, a method of generating a computer-generated reality (CGR) file includes receiving, via one or more input devices, user input generating a computer-generated reality (CGR) scene, a user input associating an anchor with the CGR scene, user input associating one or more CGR objects with the CGR scene, wherein the CGR objects are to be displayed in association with the anchor, and user input associating a behavior with the CGR scene, wherein the behavior includes one or more triggers and actions and wherein the actions are performed in response to detecting any of the triggers. The method includes generating a CGR file including data regarding the CGR scene, the CGR file including data regarding the anchor, the CGR objects, and the behavior.

Abstract: In some implementations, a method is performed by a device including a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, the method includes obtaining user movement information. In some implementations, the user movement information characterizes a first body pose of the user at a first time and a second body pose of the user at a second time. In some implementations, the method includes determining whether a transition from the first body pose to the second body pose satisfies an acceptability threshold. In some implementations, the method includes in response to determining that the transition from the first body pose to the second body pose satisfies the acceptability threshold, generating a sequence of movements for a synthesized reality (SR) representation of the user.

Abstract: In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

Abstract: Various implementations disclosed herein include devices, systems, and methods for configuring objective-effectuators. A device includes a display, a non-transitory memory and one or more processors coupled with the display and the non-transitory memory. A method includes, while displaying a computer-generated reality (CGR) representation of a first objective-effectuator in a CGR environment, determining to display a CGR representation of a second objective-effectuator in association with the CGR representation of the first objective-effectuator. In some implementations, the second objective-effectuator is associated with a set of configuration parameters. In some implementations, the method includes determining a value for at least a first configuration parameter of the set of configuration parameters based on a type of the first objective-effectuator.

Abstract: In some implementations, a method is performed by a device including a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, the method includes obtaining user movement information. In some implementations, the user movement information characterizes a first body pose of the user at a first time and a second body pose of the user at a second time. In some implementations, the method includes determining whether a transition from the first body pose to the second body pose satisfies an acceptability threshold. In some implementations, the method includes in response to determining that the transition from the first body pose to the second body pose satisfies the acceptability threshold, generating a sequence of movements for a synthesized reality (SR) representation of the user.

Abstract: In one implementation, a method of generating environment states is performed by a device including one or more processors and non-transitory memory. The method includes displaying an environment including an asset associated with a neural network model and having a plurality of asset states. The method includes receiving a user input indicative of a training request. The method includes selecting, based on the user input, a training focus indicating one or more of the plurality of asset states. The method includes generating a set of training data including a plurality of training instances weighted according to the training focus. The method includes training the neural network model on the set of training data.

Abstract: In one implementation, a method of generating an environment state is performed by a device including one or more processors and non-transitory memory. The method includes obtaining a first environment state of an environment, wherein the first environment state indicates the inclusion in the environment of a first asset associated with a first timescale value and a second asset associated with a second timescale value, wherein the first environment state further indicates that the first asset has a first state of the first asset and the second asset has a first state of the second asset. The method includes determining a second state of the first asset and the second asset based on the first and second timescale value. The method includes determining a second environment state that indicates that the first asset has the second state and the second asset has the second state.

Abstract: In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

Abstract: In one embodiment, a method of generating a computer-generated reality (CGR) file includes receiving, via one or more input devices, user input generating a computer-generated reality (CGR) scene, a user input associating an anchor with the CGR scene, user input associating one or more CGR objects with the CGR scene, wherein the CGR objects are to be displayed in association with the anchor, and user input associating a behavior with the CGR scene, wherein the behavior includes one or more triggers and actions and wherein the actions are performed in response to detecting any of the triggers. The method includes generating a CGR file including data regarding the CGR scene, the CGR file including data regarding the anchor, the CGR objects, and the behavior.