COMPUTER VISION

E2EVEEnd-to-End Visual Editing with a Generatively Pre-Trained Artist

October 05, 2022

Abstract

We consider the targeted image editing problem: blending a region in a source image with a driver image that specifies the desired change. Differently from prior works, we solve this problem by learning a conditional probability distribution of the edits, end-to-end. Training such a model requires addressing a fundamental technical challenge: the lack of example edits for training. To this end, we propose a self-supervised approach that simulates edits by augmenting off-the-shelf images in a target domain. The benefits are remarkable: implemented as a state-of-the-art auto-regressive transformer, our approach is simple, sidesteps difficulties with previous methods based on GAN-like priors, obtains significantly better edits, and is efficient. Furthermore, we show that different blending effects can be learned by an intuitive control of the augmentation process, with no other changes required to the model architecture. We demonstrate the superiority of this approach across several datasets in extensive quantitative and qualitative experiments, including human studies, significantly outperforming prior work.

Download the Paper

AUTHORS

Written by

Cheng-Yang Fu

Andrea Vedaldi

Andrew Brown

Omkar Parkhi

Tamara Berg

Publisher

ECCV

Research Topics

Computer Vision

Related Publications

March 20, 2024

COMPUTER VISION

SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model

Armen Avetisyan, Chris Xie, Henry Howard-Jenkins, Tsun-Yi Yang, Samir Aroudj, Suvam Patra, Fuyang Zhang, Duncan Frost, Luke Holland, Campbell Orme, Jakob Julian Engel, Edward Miller, Richard Newcombe, Vasileios Balntas

March 20, 2024

February 13, 2024

GRAPHICS

COMPUTER VISION

IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation

Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos

February 13, 2024

January 25, 2024

COMPUTER VISION

LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks

Felix Xu, Di Lin, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Wei Feng, Xuhong Ren

January 25, 2024

December 08, 2023

COMPUTER VISION

Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment

Sherry Xue, Kristen Grauman

December 08, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.