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    SYNTHESIZING HIGH RESOLUTION 3D SHAPES FROM LOWER RESOLUTION REPRESENTATIONS FOR SYNTHETIC DATA GENERATION SYSTEMS AND APPLICATIONS
    22.
    发明申请
    SYNTHESIZING HIGH RESOLUTION 3D SHAPES FROM LOWER RESOLUTION REPRESENTATIONS FOR SYNTHETIC DATA GENERATION SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20220392162A1

    公开(公告)日：2022-12-08

    申请号：US17718172

    申请日：2022-04-11

    Applicant: NVIDIA Corporation

    Inventor： Tianchang Shen ,  Jun Gao ,  Kangxue Yin ,  Ming-Yu Liu ,  Sanja Fidler

    IPC: G06T17/20 ,  G06T7/50

    Abstract: In various examples, a deep three-dimensional (3D) conditional generative model is implemented that can synthesize high resolution 3D shapes using simple guides—such as coarse voxels, point clouds, etc.—by marrying implicit and explicit 3D representations into a hybrid 3D representation. The present approach may directly optimize for the reconstructed surface, allowing for the synthesis of finer geometric details with fewer artifacts. The systems and methods described herein may use a deformable tetrahedral grid that encodes a discretized signed distance function (SDF) and a differentiable marching tetrahedral layer that converts the implicit SDF representation to an explicit surface mesh representation. This combination allows joint optimization of the surface geometry and topology as well as generation of the hierarchy of subdivisions using reconstruction and adversarial losses defined explicitly on the surface mesh.

    IMAGE GENERATION USING ONE OR MORE NEURAL NETWORKS
    24.
    发明申请
    IMAGE GENERATION USING ONE OR MORE NEURAL NETWORKS 有权

    公开(公告)号：US20220114698A1

    公开(公告)日：2022-04-14

    申请号：US17065780

    申请日：2020-10-08

    Applicant: Nvidia Corporation

    Inventor： Ming-Yu Liu

    IPC: G06T3/40 ,  G06T7/11

    Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to adjust one or more aspect ratios of one or more objects of one or more images based, at least in part, on input from one or more users.

    SEMANTIC IMAGE SYNTHESIS FOR GENERATING SUBSTANTIALLY PHOTOREALISTIC IMAGES USING NEURAL NETWORKS
    27.
    发明申请
    SEMANTIC IMAGE SYNTHESIS FOR GENERATING SUBSTANTIALLY PHOTOREALISTIC IMAGES USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200242774A1

    公开(公告)日：2020-07-30

    申请号：US16721852

    申请日：2019-12-19

    Applicant: Nvidia Corporation

    Inventor： Taesung Park ,  Ming-Yu Liu ,  Ting-Chun Wang ,  Junyan Zhu

    IPC: G06T7/11 ,  G06N3/08 ,  G06N20/10 ,  G06N3/04

    Abstract: A user can create a basic semantic layout that includes two or more regions identified by the user, each region being associated with a semantic label indicating a type of object(s) to be rendered in that region. The semantic layout can be provided as input to an image synthesis network. The network can be a trained machine learning network, such as a generative adversarial network (GAN), that includes a conditional, spatially-adaptive normalization layer for propagating semantic information from the semantic layout to other layers of the network. The synthesis can involve both normalization and de-normalization, where each region of the layout can utilize different normalization parameter values. An image is inferred from the network, and rendered for display to the user. The user can change labels or regions in order to cause a new or updated image to be generated.

    LEARNING TO GENERATE SYNTHETIC DATASETS FOR TRANING NEURAL NETWORKS
    28.
    发明申请
    LEARNING TO GENERATE SYNTHETIC DATASETS FOR TRANING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200160178A1

    公开(公告)日：2020-05-21

    申请号：US16685795

    申请日：2019-11-15

    Applicant: NVIDIA Corporation

    Inventor： Amlan Kar ,  Aayush Prakash ,  Ming-Yu Liu ,  David Jesus Acuna Marrero ,  Antonio Torralba Barriuso ,  Sanja Fidler

    IPC: G06N3/08 ,  G06F16/901 ,  G06N3/04 ,  G06T11/60

    Abstract: In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar—and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.

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