-
公开(公告)号：US20230306675A1
公开(公告)日：2023-09-28
申请号：US17656778
申请日：2022-03-28
Applicant: Snap Inc.
Inventor： Zeng Huang , Jian Ren , Sergey Tulyakov , Menglei Chai , Kyle Olszewski , Huan Wang
CPC classification number: G06T15/06 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.
-
Patent Agency Ranking
公开(公告)号：US20240029346A1
公开(公告)日：2024-01-25
申请号：US17814063
申请日：2022-07-21
Applicant: Snap Inc.
Inventor： Zeng Huang , Menglei Chai , Sergey Tulyakov , Kyle Olszewski , Hsin-Ying Lee
CPC classification number: G06T17/00 , G06T15/04 , G06T2207/10028
Abstract: A system to enable 3D hair reconstruction and rendering from a single reference image which performs a multi-stage process that utilizes both a 3D implicit representation and a 2D parametric embedding space.
-
公开(公告)号：US12094073B2
公开(公告)日：2024-09-17
申请号：US17814391
申请日：2022-07-22
Applicant: Snap Inc.
Inventor： Menglei Chai , Sergey Tulyakov , Jian Ren , Hsin-Ying Lee , Kyle Olszewski , Zeng Huang , Zezhou Cheng
CPC classification number: G06T19/20 , G06T17/00 , G06T2219/2012 , G06T2219/2021
Abstract: Systems, computer readable media, and methods herein describe an editing system where a three-dimensional (3D) object can be edited by editing a 2D sketch or 2D RGB views of the 3D object. The editing system uses multi-modal (MM) variational auto-decoders (VADs)(MM-VADs) that are trained with a shared latent space that enables editing 3D objects by editing 2D sketches of the 3D objects. The system determines a latent code that corresponds to an edited or sketched 2D sketch. The latent code is then used to generate a 3D object using the MM-VADs with the latent code as input. The latent space is divided into a latent space for shapes and a latent space for colors. The MM-VADs are trained with variational auto-encoders (VAE) and a ground truth.
-
公开(公告)号：US20240273809A1
公开(公告)日：2024-08-15
申请号：US18644653
申请日：2024-04-24
Applicant: Snap Inc.
Inventor： Zeng Huang , Jian Ren , Sergey Tulyakov , Menglei Chai , Kyle Olszewski , Huan Wang
CPC classification number: G06T15/06 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.
-
公开(公告)号：US12002146B2
公开(公告)日：2024-06-04
申请号：US17656778
申请日：2022-03-28
Applicant: Snap Inc.
Inventor： Zeng Huang , Jian Ren , Sergey Tulyakov , Menglei Chai , Kyle Olszewski , Huan Wang
CPC classification number: G06T15/06 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.
-
公开(公告)号：US20230386158A1
公开(公告)日：2023-11-30
申请号：US17814391
申请日：2022-07-22
Applicant: Snap Inc.
Inventor： Menglei Chai , Sergey Tulyakov , Jian Ren , Hsin-Ying Lee , Kyle Olszewski , Zeng Huang , Zezhou Cheng
CPC classification number: G06T19/20 , G06T17/00 , G06T2219/2012 , G06T2219/2021
Abstract: Systems, computer readable media, and methods herein describe an editing system where a three-dimensional (3D) object can be edited by editing a 2D sketch or 2D RGB views of the 3D object. The editing system uses multi-modal (MM) variational auto-decoders (VADs)(MM-VADs) that are trained with a shared latent space that enables editing 3D objects by editing 2D sketches of the 3D objects. The system determines a latent code that corresponds to an edited or sketched 2D sketch. The latent code is then used to generate a 3D object using the MM-VADs with the latent code as input. The latent space is divided into a latent space for shapes and a latent space for colors. The MM-VADs are trained with variational auto-encoders (VAE) and a ground truth.
-
-
-
-
-
Search Results
6
records
(took 0.014 seconds)
