Patents by Inventor Zan Gojcic

Zan Gojcic has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240153188
    Abstract: In various examples, systems and methods are disclosed relating to generating physics-plausible whole body motion, including determining a mesh sequence corresponding to a motion of at least one dynamic character of one or more dynamic characters and a mesh of a terrain using a video sequence, determining using a generative model and based at least one the mesh sequence and the mesh of the terrain, an occlusion-free motion of the at least one dynamic character by infilling physics-plausible character motions in the mesh sequence for at least one frame of the video sequence that includes an occlusion of at least a portion of the at least one dynamic character, and determining physics-plausible whole body motion of the at least one dynamic character by applying physics-based imitation upon the occlusion-free motion.
    Type: Application
    Filed: August 24, 2023
    Publication date: May 9, 2024
    Applicant: NVIDIA Corporation
    Inventors: Jingbo WANG, Ye YUAN, Cheng XIE, Sanja FIDLER, Jan KAUTZ, Umar IQBAL, Zan GOJCIC, Sameh KHAMIS
  • Publication number: 20240096017
    Abstract: Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate one or more textured three-dimensional meshes corresponding to one or more objects based, at least in part, one or more two-dimensional images of the one or more objects.
    Type: Application
    Filed: August 25, 2022
    Publication date: March 21, 2024
    Inventors: Jun Gao, Tianchang Shen, Zan Gojcic, Wenzheng Chen, Zian Wang, Daiqing Li, Or Litany, Sanja Fidler
  • Publication number: 20240005604
    Abstract: Approaches presented herein provide for the unconditional generation of novel three dimensional (3D) object shape representations, such as point clouds or meshes. In at least one embodiment, a first denoising diffusion model (DDM) can be trained to synthesize a 1D shape latent from Gaussian noise, and a second DDM can be trained to generate a set of latent points conditioned on this 1D shape latent. The shape latent and set of latent points can be provided to a decoder to generate a 3D point cloud representative of a random object from among the object classes on which the models were trained. A surface reconstruction process may be used to generate a surface mesh from this generated point cloud. Such an approach can scale to complex and/or multimodal distributions, and can be highly flexible as it can be adapted to various tasks such as multimodal voxel- or text-guided synthesis.
    Type: Application
    Filed: May 19, 2023
    Publication date: January 4, 2024
    Inventors: Karsten Julian Kreis, Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler