MOWA: Multiple-in-One Image Warping Model

Introduction

This is the official implementation for MOWA (arXiv 2024).

Kang Liao, Zongsheng Yue, Zhonghua Wu, Chen Change Loy

S-Lab, Nanyang Technological University

Why MOWA?

MOWA is a practical multiple-in-one image warping framework, particularly in computational photography, where six distinct tasks are considered. Compared to previous works tailored to specific tasks, our method can solve various warping tasks from different camera models or manipulation spaces in a single framework. It also demonstrates an ability to generalize to novel scenarios, as evidenced in both cross-domain and zero-shot evaluations.

Features

• The first practical multiple-in-one image warping framework especially in the field of computational photograpghy.
• We propose to mitigate the difficulty of multi-task learning by decoupling the motion estimation in both the region level and pixel level.
• A prompt learning module, guided by a lightweight point-based classifier, is designed to facilitate task-aware image warpings.

Code

Coming soon.