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    PERFORMING OCCLUSION-AWARE GLOBAL 3D POSE AND SHAPE ESTIMATION OF ARTICULATED OBJECTS
    73.
    发明申请
    PERFORMING OCCLUSION-AWARE GLOBAL 3D POSE AND SHAPE ESTIMATION OF ARTICULATED OBJECTS 有权

    公开(公告)号：US20230070514A1

    公开(公告)日：2023-03-09

    申请号：US17584213

    申请日：2022-01-25

    Applicant: NVIDIA Corporation

    Inventor： Ye Yuan ,  Umar Iqbal ,  Pavlo Molchanov ,  Jan Kautz

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

    Abstract: In order to determine accurate three-dimensional (3D) models for objects within a video, the objects are first identified and tracked within the video, and a pose and shape are estimated for these tracked objects. A translation and global orientation are removed from the tracked objects to determine local motion for the objects, and motion infilling is performed to fill in any missing portions for the object within the video. A global trajectory is then determined for the objects within the video, and the infilled motion and global trajectory are then used to determine infilled global motion for the object within the video. This enables the accurate depiction of each object as a 3D pose sequence for that model that accounts for occlusions and global factors within the video.

    IMAGE PROCESSING USING COUPLED SEGMENTATION AND EDGE LEARNING
    76.
    发明申请
    IMAGE PROCESSING USING COUPLED SEGMENTATION AND EDGE LEARNING 有权

    公开(公告)号：US20230015989A1

    公开(公告)日：2023-01-19

    申请号：US17365877

    申请日：2021-07-01

    Applicant: Nvidia Corporation

    Inventor： Zhiding Yu ,  Rui Huang ,  Wonmin Byeon ,  Sifei Liu ,  Guilin Liu ,  Thomas Breuel ,  Anima Anandkumar ,  Jan Kautz

    IPC: G06K9/46 ,  G06N3/04 ,  G06T7/13 ,  G06K9/62

    Abstract: The disclosure provides a learning framework that unifies both semantic segmentation and semantic edge detection. A learnable recurrent message passing layer is disclosed where semantic edges are considered as explicitly learned gating signals to refine segmentation and improve dense prediction quality by finding compact structures for message paths. The disclosure includes a method for coupled segmentation and edge learning. In one example, the method includes: (1) receiving an input image, (2) generating, from the input image, a semantic feature map, an affinity map, and a semantic edge map from a single backbone network of a convolutional neural network (CNN), and (3) producing a refined semantic feature map by smoothing pixels of the semantic feature map using spatial propagation, and controlling the smoothing using both affinity values from the affinity map and edge values from the semantic edge map.

    Three-dimensional (3D) pose estimation from a monocular camera
    79.
    发明授权
    Three-dimensional (3D) pose estimation from a monocular camera 有权

    公开(公告)号：US11488418B2

    公开(公告)日：2022-11-01

    申请号：US17135697

    申请日：2020-12-28

    Applicant: NVIDIA Corporation

    Inventor： Umar Iqbal ,  Pavlo Molchanov ,  Thomas Michael Breuel ,  Jan Kautz

    IPC: G06V40/20 ,  G06N3/08 ,  G06T7/73 ,  G06N5/04 ,  G06T7/579 ,  G06V40/10

    Abstract: Estimating a three-dimensional (3D) pose of an object, such as a hand or body (human, animal, robot, etc.), from a 2D image is necessary for human-computer interaction. A hand pose can be represented by a set of points in 3D space, called keypoints. Two coordinates (x,y) represent spatial displacement and a third coordinate represents a depth of every point with respect to the camera. A monocular camera is used to capture an image of the 3D pose, but does not capture depth information. A neural network architecture is configured to generate a depth value for each keypoint in the captured image, even when portions of the pose are occluded, or the orientation of the object is ambiguous. Generation of the depth values enables estimation of the 3D pose of the object.

    THREE-DIMENSIONAL OBJECT RECONSTRUCTION FROM A VIDEO
    80.
    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.

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