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    THREE-DIMENSIONAL OBJECT RECONSTRUCTION FROM A VIDEO
    12.
    发明申请
    THREE-DIMENSIONAL OBJECT RECONSTRUCTION FROM A VIDEO 有权

    公开(公告)号：US20220036635A1

    公开(公告)日：2022-02-03

    申请号：US16945455

    申请日：2020-07-31

    Applicant: NVIDIA Corporation

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

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

    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.

    LEARNING AFFINITY VIA A SPATIAL PROPAGATION NEURAL NETWORK
    15.
    发明申请
    LEARNING AFFINITY VIA A SPATIAL PROPAGATION NEURAL NETWORK 审中-公开

    公开(公告)号：US20190095791A1

    公开(公告)日：2019-03-28

    申请号：US16134716

    申请日：2018-09-18

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Shalini De Mello ,  Jinwei Gu ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06N3/08 ,  G06K9/46 ,  G06N5/04 ,  G06T7/90

    Abstract: A spatial linear propagation network (SLPN) system learns the affinity matrix for vision tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The SLPN system is trained for a particular computer vision task and refines an input map (i.e., affinity matrix) that indicates pixels the share a particular property (e.g., color, object, texture, shape, etc.). Inputs to the SLPN system are input data (e.g., pixel values for an image) and the input map corresponding to the input data to be propagated. The input data is processed to produce task-specific affinity values (guidance data). The task-specific affinity values are applied to values in the input map, with at least two weighted values from each column contributing to a value in the refined map data for the adjacent column.

    SAMPLING TECHNIQUE TO SCALE NEURAL VOLUME RENDERING TO HIGH RESOLUTION
    17.
    发明申请
    SAMPLING TECHNIQUE TO SCALE NEURAL VOLUME RENDERING TO HIGH RESOLUTION 有权

    公开(公告)号：US20250111474A1

    公开(公告)日：2025-04-03

    申请号：US18830914

    申请日：2024-09-11

    Applicant: NVIDIA Corporation

    Inventor： Koki Nagano ,  Alexander Trevithick ,  Matthew Aaron Wong Chan ,  Towaki Takikawa ,  Umar Iqbal ,  Shalini De Mello

    IPC: G06T3/4046 ,  G06T5/60 ,  G06T5/70 ,  G06T15/08

    Abstract: Systems and methods are disclosed that relate to synthesizing high-resolution 3D geometry and strictly view-consistent images that maintain image quality without relying on post-processing super resolution. For instance, embodiments of the present disclosure describe techniques, systems, and/or methods to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Embodiments of the present disclosure employ learning-based samplers for accelerating neural rendering for 3D GAN training using up to five times fewer depth samples, which enables embodiments of the present disclosure to explicitly “render every pixel” of the full-resolution image during training and inference without post-processing super-resolution in 2D. Together with learning high-quality surface geometry, embodiments of the present disclosure synthesize high-resolution 3D geometry and strictly view—consistent images while maintaining image quality on par with baselines relying on post-processing super resolution.

    LEARNING DENSE CORRESPONDENCES FOR IMAGES
    19.
    发明公开
    LEARNING DENSE CORRESPONDENCES FOR IMAGES 审中-公开

    公开(公告)号：US20230252692A1

    公开(公告)日：2023-08-10

    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 ,  G06T3/00

    CPC classification number: G06T11/001 ,  G06T3/0093

    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.

    PERFORMING SEMANTIC SEGMENTATION TRAINING WITH IMAGE/TEXT PAIRS
    20.
    发明公开
    PERFORMING SEMANTIC SEGMENTATION TRAINING WITH IMAGE/TEXT PAIRS 审中-公开

    公开(公告)号：US20230177810A1

    公开(公告)日：2023-06-08

    申请号：US17853631

    申请日：2022-06-29

    Applicant: NVIDIA Corporation

    Inventor： Jiarui Xu ,  Shalini De Mello ,  Sifei Liu ,  Wonmin Byeon ,  Thomas Breuel ,  Jan Kautz

    IPC: G06V10/774 ,  G06V10/26

    CPC classification number: G06V10/774 ,  G06V10/26

    Abstract: Semantic segmentation includes the task of providing pixel-wise annotations for a provided image. To train a machine learning environment to perform semantic segmentation, image/caption pairs are retrieved from one or more databases. These image/caption pairs each include an image and associated textual caption. The image portion of each image/caption pair is passed to an image encoder of the machine learning environment that outputs potential pixel groupings (e.g., potential segments of pixels) within each image, while nouns are extracted from the caption portion and are converted to text prompts which are then passed to a text encoder that outputs a corresponding text representation. Contrastive loss operations are then performed on features extracted from these pixel groupings and text representations to determine an extracted feature for each noun of each caption that most closely matches the extracted features for the associated image.

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