Responding to representations of physical elements

    公开(公告)号:US12277621B2

    公开(公告)日:2025-04-15

    申请号:US17465334

    申请日:2021-09-02

    Applicant: Apple Inc.

    Abstract: In some implementations, a method includes obtaining, by a virtual intelligent agent (VIA), a perceptual property vector (PPV) for a graphical representation of a physical element. In some implementations, the PPV includes one or more perceptual characteristic values characterizing the graphical representation of the physical element. In some implementations, the method includes instantiating a graphical representation of the VIA in a graphical environment that includes the graphical representation of the physical element and an affordance that is associated with the graphical representation of the physical element. In some implementations, the method includes generating, by the VIA, an action for the graphical representation of the VIA based on the PPV. In some implementations, the method includes displaying a manipulation of the affordance by the graphical representation of the VIA in order to effectuate the action generated by the VIA.

    Method and device for generating a blended animation

    公开(公告)号:US11593982B1

    公开(公告)日:2023-02-28

    申请号:US17557273

    申请日:2021-12-21

    Applicant: Apple Inc.

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

    Controlling joints using learned torques

    公开(公告)号:US11430170B1

    公开(公告)日:2022-08-30

    申请号:US17173480

    申请日:2021-02-11

    Applicant: Apple Inc.

    Abstract: A method includes obtaining video data including a representation of a first plurality of motions of a real-world entity. The method includes determining, from the video data, a plurality of estimated torque values using a motion controller. The plurality of estimated torque values is associated with a plurality of real-world joints corresponding to the first plurality of motions. The method includes generating a second plurality of motions of a virtual agent by providing the plurality of estimated torque values to a corresponding plurality of virtual joints of the virtual agent. Movement of the virtual agent is controllable by the plurality of virtual joints, corresponding to the plurality of real-world joints of the real-world entity. The method includes, responsive to a determination that a comparison between the first plurality of motions and the second plurality of motions does not satisfy a performance metric, changing an operational value of the motion controller.

    Granular motion control for a virtual agent

    公开(公告)号:US11776193B1

    公开(公告)日:2023-10-03

    申请号:US17215115

    申请日:2021-03-29

    Applicant: Apple Inc.

    Abstract: Various implementations disclosed herein include devices, systems, and methods for granular motion control for a virtual agent. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes obtaining an action for a virtual agent. In some implementations, the action is associated with a plurality of time frames. In some implementations, the method includes, for a first time frame of the plurality of time frames, determining respective confidence scores for a plurality of granular motions that advance the virtual agent towards completion of the action. In some implementations, the method includes selecting a subset of the plurality of granular motions based on the respective confidence scores.

    Responding to Representations of Physical Elements

    公开(公告)号:US20210398327A1

    公开(公告)日:2021-12-23

    申请号:US17465334

    申请日:2021-09-02

    Applicant: Apple Inc.

    Abstract: In some implementations, a method includes obtaining, by a virtual intelligent agent (VIA), a perceptual property vector (PPV) for a graphical representation of a physical element. In some implementations, the PPV includes one or more perceptual characteristic values characterizing the graphical representation of the physical element. In some implementations, the method includes instantiating a graphical representation of the VIA in a graphical environment that includes the graphical representation of the physical element and an affordance that is associated with the graphical representation of the physical element. In some implementations, the method includes generating, by the VIA, an action for the graphical representation of the VIA based on the PPV. In some implementations, the method includes displaying a manipulation of the affordance by the graphical representation of the VIA in order to effectuate the action generated by the VIA.

    GENERATING DIRECTIVES FOR OBJECTIVE-EFFECTUATORS

    公开(公告)号:US20210272381A1

    公开(公告)日:2021-09-02

    申请号:US17325454

    申请日:2021-05-20

    Applicant: Apple Inc.

    Abstract: A method includes generating, in coordination with an emergent content engine, a first objective for a first objective-effectuator and a second objective for a second objective-effectuator instantiated in a computer-generated reality (CGR) environment. The first and second objectives are associated with a mutual plan. The method includes generating, based on characteristic values associated with the first and second objective-effectuators a first directive for the first objective-effectuator and a second directive for the second objective-effectuator. The first directive limits actions generated by the first objective-effectuator over a first set of time frames associated with the first objective and the second directive limits actions generated by the second objective-effectuator over a second set of time frames associated with the second objective. The method includes displaying manipulations of CGR representations of the first and second objective-effectuators in the CGR environment in accordance with the first and second directives.

    Generating directives for objective-effectuators

    公开(公告)号:US11055930B1

    公开(公告)日:2021-07-06

    申请号:US16862998

    申请日:2020-04-30

    Applicant: Apple Inc.

    Abstract: A method includes generating, in coordination with an emergent content engine, a first objective for a first objective-effectuator and a second objective for a second objective-effectuator instantiated in a computer-generated reality (CGR) environment. The first and second objectives are associated with a mutual plan. The method includes generating, based on characteristic values associated with the first and second objective-effectuators a first directive for the first objective-effectuator and a second directive for the second objective-effectuator. The first directive limits actions generated by the first objective-effectuator over a first set of time frames associated with the first objective and the second directive limits actions generated by the second objective-effectuator over a second set of time frames associated with the second objective. The method includes displaying manipulations of CGR representations of the first and second objective-effectuators in the CGR environment in accordance with the first and second directives.

    Perceptual property vector for an object

    公开(公告)号:US11961191B2

    公开(公告)日:2024-04-16

    申请号:US17465320

    申请日:2021-09-02

    Applicant: Apple Inc.

    CPC classification number: G06T19/006 G06T15/04 G06T15/08 G06V20/41

    Abstract: In some implementations, a method includes obtaining a semantic construction of a physical environment. In some implementations, the semantic construction of the physical environment includes a representation of a physical element and a semantic label for the physical element. In some implementations, the method includes obtaining a graphical representation of the physical element. In some implementations, the method includes synthesizing a perceptual property vector (PPV) for the graphical representation of the physical element based on the semantic label for the physical element. In some implementations, the PPV includes one or more perceptual characteristic values characterizing the graphical representation of the physical element. In some implementations, the method includes compositing an affordance in association with the graphical representation of the physical element. In some implementations, the affordance allows interaction with the graphical representation of the physical element in accordance with the perceptual characteristic values included in the PPV.

    Method and device for modeling a behavior with synthetic training data

    公开(公告)号:US11797889B1

    公开(公告)日:2023-10-24

    申请号:US17557288

    申请日:2021-12-21

    Applicant: Apple Inc.

    CPC classification number: G06N20/00 G06T17/00

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

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