Disambiguation of poses
    61.
    发明授权

    公开(公告)号:US11763508B2

    公开(公告)日:2023-09-19

    申请号:US17095481

    申请日:2020-11-11

    Abstract: Computer animation involving pose disambiguation is disclosed. Two or more source segmentation masks are generated from corresponding contemporaneous video images of a character from different points of view. A three-dimensional model of an animation character corresponding to the character in the two or more contemporaneous video images is generated. Two or more different target segmentation masks corresponding to different views of the animation character corresponding to the character in the two or more video images. Each target segmentation mask is compared to a corresponding source segmentation mask from the comparison it is determined whether a pose of the three-dimensional model corresponds to a pose of the character in the video images. The model is used to generate a frame of animation of the animated character when the pose of model corresponds to the pose of the character in the video images.

    Dynamic modification of audio playback in games

    公开(公告)号:US11638873B2

    公开(公告)日:2023-05-02

    申请号:US17145216

    申请日:2021-01-08

    Abstract: A method for dynamically modifying audio playback of a video game is provided. The method includes ascertaining a game music data associated with the video game. The game music data may include a plurality of game soundtracks classified according to predetermined criteria. The method may include collecting feedback over a period of time while the user is playing the video game. The method may continue with determining, based on the feedback, one or more replacement soundtracks based on criteria associated with the one or more replacement soundtracks. The method may further include dynamically modifying the game music data while the user is playing the video game by replacing at least one of the plurality of game soundtracks with the one or more replacement soundtracks to obtain modified game music data. The modified game music data may be provided to the user while the user is playing the video game.

    Projecting content onto water to enhance computer simulation

    公开(公告)号:US11632529B2

    公开(公告)日:2023-04-18

    申请号:US17200718

    申请日:2021-03-12

    Abstract: Content is projected onto the surface of water such that it can be viewed from either above or below the surface. Using distortion mapping, depth sensing, and de-noising, the image can remain unaffected by ripples and allow the user to interact within the body of liquid and/or use it as input. Liquids of different density can be layered on the surface to create different refraction planes. Water currents and jets can be used to actively reduce ripples from user interaction, as well as actively adding or removing liquid from the container to counteract displacement or create effect.

    METHOD FOR ROBOTIC TRAINING BASED ON RANDOMIZATION OF SURFACE DAMPING

    公开(公告)号:US20220143822A1

    公开(公告)日:2022-05-12

    申请号:US17095640

    申请日:2020-11-11

    Abstract: A method, system and computer product for training a control input system involve taking an integral of an output value from a Motion Decision Neural Network for one or more movable joints to generate an integrated output value and generating a subsequent output value using a machine learning algorithm that includes a sensor value and a previous joint position if the integrated output value does not at least meet the threshold. Surface damping interactions with at least a simulated environment, a rigid body position and a position of the one or more movable joints based on an integral of the subsequent output value are simulated. The Motion Decision Neural Network is trained with the machine learning algorithm based upon at least a result of the simulation of the simulated environment and position of the one or more movable joints.

    METHOD FOR ROBOTIC TRAINING BASED ON RANDOMIZATION OF SURFACE STIFFNESS

    公开(公告)号:US20220143821A1

    公开(公告)日:2022-05-12

    申请号:US17095617

    申请日:2020-11-11

    Abstract: A method, system and computer product for training a control input system involve taking an integral of an output value from a Motion Decision Neural Network for one or more movable joints to generate an integrated output value and generating a subsequent output value using a machine learning algorithm that includes a sensor value and a previous joint position if the integrated output value does not at least meet the threshold. Surface stiffness interactions with at least a simulated environment, a rigid body position and a position of the one or more movable joints based on an integral of the subsequent output value are simulated. The Motion Decision Neural Network is trained with the machine learning algorithm based upon at least a result of the simulation of the simulated environment and position of the one or more movable joints.

    Binocular pose prediction
    68.
    发明授权

    公开(公告)号:US11263796B1

    公开(公告)日:2022-03-01

    申请号:US17095518

    申请日:2020-11-11

    Abstract: Computer animation involving monocular pose prediction is disclosed. A plurality of candidate pose sequences of a three-dimensional model of an animation character is generated such that each candidate pose of each sequence has a segmentation map that matches a segmentation map of a corresponding character derived from a corresponding frame of a video. A distance between candidate poses at each time step is maximized. An optimum pose sequence is determined and used to generate a corresponding sequence of frames of animation.

    Reinforcement learning to train a character using disparate target animation data

    公开(公告)号:US11132606B2

    公开(公告)日:2021-09-28

    申请号:US16355680

    申请日:2019-03-15

    Inventor: Michael Taylor

    Abstract: A method for training an animation character, including mapping first animation data defining a first motion sequence to a first subset of bones of a trained character, and mapping second animation data defining a second motion sequence to a second subset of bones. A bone hierarchy includes the first subset of bones and second subset of bones. Reinforcement learning is applied iteratively for training the first subset of bones using the first animation data and for training the second subset of bones using the second animation data. Training of each subset of bones is performed concurrently at each iteration. Training includes adjusting orientations of bones. The first subset of bones is composited with the second subset of bones at each iteration by applying physics parameters of a simulation environment to the adjusted orientations of bones in the first and second subset of bones.

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