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    SYSTEMS AND METHODS FOR IMAGE-TO-IMAGE TRANSLATION USING VARIATIONAL AUTOENCODERS
    62.
    发明申请
    SYSTEMS AND METHODS FOR IMAGE-TO-IMAGE TRANSLATION USING VARIATIONAL AUTOENCODERS 审中-公开

    公开(公告)号：US20180247201A1

    公开(公告)日：2018-08-30

    申请号：US15907098

    申请日：2018-02-27

    Applicant: NVIDIA Corporation

    Inventor： Ming-Yu Liu ,  Thomas Michael Breuel ,  Jan Kautz

    IPC: G06N3/08 ,  G06N3/04 ,  G06T3/40

    CPC classification number: G06N3/088 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/063 ,  G06N3/084 ,  G06T1/00 ,  G06T3/4046

    Abstract: A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of encoding, by a first neural network, a first image represented in a first domain to convert the first image to a shared latent space, producing a first latent code and encoding, by a second neural network, a second image represented in a second domain to convert the second image to a shared latent space, producing a second latent code. The method also includes the step of generating, by a third neural network, a first translated image in the second domain based on the first latent code, wherein the first translated image is correlated with the first image and weight values of the third neural network are computed based on the first latent code and the second latent code.

    PHYSICS-GUIDED MOTION DIFFUSION MODEL
    66.
    发明公开
    PHYSICS-GUIDED MOTION DIFFUSION MODEL 审中-公开

    公开(公告)号：US20240169636A1

    公开(公告)日：2024-05-23

    申请号：US18317378

    申请日：2023-05-15

    Applicant: NVIDIA Corporation

    Inventor： Ye Yuan ,  Jiaming Song ,  Umar Iqbal ,  Arash Vahdat ,  Jan Kautz

    IPC: G06T13/40 ,  G06T5/00 ,  G06T13/80

    CPC classification number: G06T13/40 ,  G06T5/002 ,  G06T13/80 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Systems and methods are disclosed that improve performance of synthesized motion generated by a diffusion neural network model. A physics-guided motion diffusion model incorporates physical constraints into the diffusion process to model the complex dynamics induced by forces and contact. Specifically, a physics-based motion projection module uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically plausible motion. The projected motion is further used in the next diffusion iteration to guide the denoising diffusion process. The use of physical constraints in the physics-guided motion diffusion model iteratively pulls the motion toward a physically-plausible space, reducing artifacts such as floating, foot sliding, and ground penetration.

    Future object trajectory predictions for autonomous machine applications
    67.
    发明授权
    Future object trajectory predictions for autonomous machine applications 有权

    公开(公告)号：US11989642B2

    公开(公告)日：2024-05-21

    申请号：US17952866

    申请日：2022-09-26

    Applicant: NVIDIA Corporation

    Inventor： Ruben Villegas ,  Alejandro Troccoli ,  Iuri Frosio ,  Stephen Tyree ,  Wonmin Byeon ,  Jan Kautz

    IPC: G06N3/044 ,  B60W40/02 ,  G06N3/045 ,  G06N3/08

    CPC classification number: G06N3/044 ,  B60W40/02 ,  G06N3/08 ,  G06N3/045

    Abstract: In various examples, historical trajectory information of objects in an environment may be tracked by an ego-vehicle and encoded into a state feature. The encoded state features for each of the objects observed by the ego-vehicle may be used—e.g., by a bi-directional long short-term memory (LSTM) network—to encode a spatial feature. The encoded spatial feature and the encoded state feature for an object may be used to predict lateral and/or longitudinal maneuvers for the object, and the combination of this information may be used to determine future locations of the object. The future locations may be used by the ego-vehicle to determine a path through the environment, or may be used by a simulation system to control virtual objects—according to trajectories determined from the future locations—through a simulation environment.

    POSE TRANSFER FOR THREE-DIMENSIONAL CHARACTERS USING A LEARNED SHAPE CODE
    68.
    发明公开
    POSE TRANSFER FOR THREE-DIMENSIONAL CHARACTERS USING A LEARNED SHAPE CODE 审中-公开

    公开(公告)号：US20240070987A1

    公开(公告)日：2024-02-29

    申请号：US18110287

    申请日：2023-02-15

    Applicant: NVIDIA Corporation

    Inventor： Xueting Li ,  Sifei Liu ,  Shalini De Mello ,  Orazio Gallo ,  Jiashun Wang ,  Jan Kautz

    IPC: G06T19/00 ,  G06T7/10 ,  G06T17/20

    CPC classification number: G06T19/00 ,  G06T7/10 ,  G06T17/20

    Abstract: Transferring pose to three-dimensional characters is a common computer graphics task that typically involves transferring the pose of a reference avatar to a (stylized) three-dimensional character. Since three-dimensional characters are created by professional artists through imagination and exaggeration, and therefore, unlike human or animal avatars, have distinct shape and features, matching the pose of a three-dimensional character to that of a reference avatar generally requires manually creating shape information for the three-dimensional character that is required for pose transfer. The present disclosure provides for the automated transfer of a reference pose to a three-dimensional character, based specifically on a learned shape code for the three-dimensional character.

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