公开(公告)号：US20240412458A1

公开(公告)日：2024-12-12

申请号：US18741680

申请日：2024-06-12

Applicant: Google LLC

Inventor： Varun Jampani ,  Chun-Han Yao ,  Amit Raj ,  Wei-Chih Hung ,  Ming-Hsuan Yang ,  Michael Rubinstein ,  Yuanzhen Li

IPC: G06T17/20 ,  G06T5/70 ,  G06T11/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for editing images based on decoder-based accumulative score sampling (DASS) losses.

公开(公告)号：US20240320912A1

公开(公告)日：2024-09-26

申请号：US18611236

申请日：2024-03-20

Applicant: Google LLC

Inventor： Yuanzhen Li ,  Amit Raj ,  Varun Jampani ,  Benjamin Joseph Mildenhall ,  Benjamin Michael Poole ,  Jonathan Tilton Barron ,  Kfir Aberman ,  Michael Niemeyer ,  Michael Rubinstein ,  Nataniel Ruiz Gutierrez ,  Shiran Elyahu Zada ,  Srinivas Kaza

IPC: G06T17/00 ,  H04N13/279 ,  H04N13/351

CPC classification number: G06T17/00 ,  H04N13/279 ,  H04N13/351

Abstract: A fractional training process can be performed training images to an instance of a machine-learned generative image model to obtain a partially trained instance of the model. A fractional optimization process can be performed with the partially trained instance to an instance of a machine-learned three-dimensional (3D) implicit representation model obtain a partially optimized instance of the model. Based on the plurality of training images, pseudo multi-view subject images can be generated with the partially optimized instance of the 3D implicit representation model and a fully trained instance of the generative image model; The partially trained instance of the model can be trained with a set of training data. The partially optimized instance of the machine-learned 3D implicit representation model can be trained with the machine-learned multi-view image model.