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    THREE-DIMENSIONAL OBJECT RECONSTRUCTION FROM A VIDEO
    33.
    发明申请
    THREE-DIMENSIONAL OBJECT RECONSTRUCTION FROM A VIDEO 有权

    公开(公告)号：US20220270318A1

    公开(公告)日：2022-08-25

    申请号：US17734244

    申请日：2022-05-02

    Applicant: NVIDIA Corporation

    Inventor： Xueting Li ,  Sifei Liu ,  Kihwan Kim ,  Shalini De Mello ,  Jan Kautz

    IPC: G06T15/04 ,  G06T7/579 ,  G06T7/70 ,  G06T17/20 ,  G06T15/20

    Abstract: A three-dimensional (3D) object reconstruction neural network system learns to predict a 3D shape representation of an object from a video that includes the object. The 3D reconstruction technique may be used for content creation, such as generation of 3D characters for games, movies, and 3D printing. When 3D characters are generated from video, the content may also include motion of the character, as predicted based on the video. The 3D object construction technique exploits temporal consistency to reconstruct a dynamic 3D representation of the object from an unlabeled video. Specifically, an object in a video has a consistent shape and consistent texture across multiple frames. Texture, base shape, and part correspondence invariance constraints may be applied to fine-tune the neural network system. The reconstruction technique generalizes well—particularly for non-rigid objects.

    Switchable propagation neural network
    35.
    发明授权
    Switchable propagation neural network 有权

    公开(公告)号：US11328169B2

    公开(公告)日：2022-05-10

    申请号：US16353835

    申请日：2019-03-14

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Shalini De Mello ,  Jinwei Gu ,  Varun Jampani ,  Jan Kautz

    IPC: G06K9/62 ,  G06V20/40 ,  G06T7/90 ,  G06T5/00 ,  G06T7/10 ,  G06N3/08 ,  G06N3/04 ,  G06T5/50

    Abstract: A temporal propagation network (TPN) system learns the affinity matrix for video image processing tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The TPN system includes a guidance neural network model and a temporal propagation module and is trained for a particular computer vision task to propagate visual properties from a key-frame represented by dense data (color), to another frame that is represented by coarse data (grey-scale). The guidance neural network model generates an affinity matrix referred to as a global transformation matrix from task-specific data for the key-frame and the other frame. The temporal propagation module applies the global transformation matrix to the key-frame property data to produce propagated property data (color) for the other frame. For example, the TPN system may be used to colorize several frames of greyscale video using a single manually colorized key-frame.

    SWITCHABLE PROPAGATION NEURAL NETWORK
    36.
    发明申请
    SWITCHABLE PROPAGATION NEURAL NETWORK 有权

    公开(公告)号：US20210073575A1

    公开(公告)日：2021-03-11

    申请号：US17081805

    申请日：2020-10-27

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Shalini De Mello ,  Jinwei Gu ,  Varun Jampani ,  Jan Kautz

    IPC: G06K9/62 ,  G06K9/00 ,  G06T7/90 ,  G06T5/00 ,  G06T7/10 ,  G06N3/08 ,  G06N3/04 ,  G06T5/50

    Abstract: A temporal propagation network (TPN) system learns the affinity matrix for video image processing tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The TPN system includes a guidance neural network model and a temporal propagation module and is trained for a particular computer vision task to propagate visual properties from a key-frame represented by dense data (color), to another frame that is represented by coarse data (grey-scale). The guidance neural network model generates an affinity matrix referred to as a global transformation matrix from task-specific data for the key-frame and the other frame. The temporal propagation module applies the global transformation matrix to the key-frame property data to produce propagated property data (color) for the other frame. For example, the TPN system may be used to colorize several frames of greyscale video using a single manually colorized key-frame.

    Learning dense correspondences for images
    40.
    发明授权
    Learning dense correspondences for images 有权

    公开(公告)号：US12169882B2

    公开(公告)日：2024-12-17

    申请号：US17929182

    申请日：2022-09-01

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Jiteng Mu ,  Shalini De Mello ,  Zhiding Yu ,  Jan Kautz

    IPC: G06T11/00 ,  G06T3/18

    Abstract: Embodiments of the present disclosure relate to learning dense correspondences for images. Systems and methods are disclosed that disentangle structure and texture (or style) representations of GAN synthesized images by learning a dense pixel-level correspondence map for each image during image synthesis. A canonical coordinate frame is defined and a structure latent code for each generated image is warped to align with the canonical coordinate frame. In sum, the structure associated with the latent code is mapped into a shared coordinate space (canonical coordinate space), thereby establishing correspondences in the shared coordinate space. A correspondence generation system receives the warped coordinate correspondences as an encoded image structure. The encoded image structure and a texture latent code are used to synthesize an image. The shared coordinate space enables propagation of semantic labels from reference images to synthesized images.

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