公开(公告)号：US20220148299A1

公开(公告)日：2022-05-12

申请号：US17438687

申请日：2019-07-19

Applicant: Google LLC

Inventor： Mikael Pierre Bonnevie ,  Aaron Maschinot ,  Aaron Sarna ,  Shuchao Bi ,  Jingbin Wang ,  Michael Spencer Krainin ,  Wenchao Tong ,  Dilip Krishnan ,  Haifeng Gong ,  Ce Liu ,  Hossein Talebi ,  Raanan Sayag ,  Piotr Teterwak

IPC: G06V10/82 ,  G06N3/04 ,  G06T7/10

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.

公开(公告)号：US12236676B2

公开(公告)日：2025-02-25

申请号：US17438687

申请日：2019-07-19

Applicant: Google LLC

Inventor： Mikael Pierre Bonnevie ,  Aaron Maschinot ,  Aaron Sarna ,  Shuchao Bi ,  Jingbin Wang ,  Michael Spencer Krainin ,  Wenchao Tong ,  Dilip Krishnan ,  Haifeng Gong ,  Ce Liu ,  Hossein Talebi ,  Raanan Sayag ,  Piotr Teterwak

IPC: G06K9/00 ,  G06N3/045 ,  G06T7/10 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.