公开(公告)号：US20240355017A1

公开(公告)日：2024-10-24

申请号：US18302508

申请日：2023-04-18

Applicant: Google LLC

Inventor： Shiran Elyahu Zada ,  Bahjat Kawar ,  Oran Lang ,  Omer Tov ,  Huiwen Chang ,  Tali Dekel ,  Inbar Mosseri

IPC: G06T11/60 ,  G06T3/40

CPC classification number: G06T11/60 ,  G06T3/4053

Abstract: Methods and systems for editing an image are disclosed herein. The method includes receiving an input image and a target text, the target text indicating a desired edit for the input image and obtaining, by the computing system, a target text embedding based on the target text. The method also includes obtaining, by the computing system, an optimized text embedding based on the target text embedding and the input image and fine-tuning, by the computing system, a diffusion model based on the optimized text embedding. The method can further include interpolating, by the computing system, the target text embedding and the optimized text embedding to obtain an interpolated embedding and generating, by the computing system, an edited image including the desired edit using the diffusion model based on the input image and the interpolated embedding.

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.