MOWA: Multiple-in-One Image Warping Model
Kang Liao Zongsheng Yue Zhonghua Wu Chen Change Loy
While recent image warping approaches achieved remarkable success on existing
benchmarks, they still require training separate models for each specific task
and cannot generalize well to different camera models or customized
manipulations. To address diverse types of warping in practice, we propose a
Multiple-in-One image WArping model (named MOWA) in this work. Specifically, we
mitigate the difficulty of multi-task learning by disentangling the motion
estimation at both the region level and pixel level. To further enable dynamic
task-aware image warping, we introduce a lightweight point-based classifier
that predicts the task type, serving as prompts to modulate the feature maps
for better estimation. To our knowledge, this is the first work that solves
multiple practical warping tasks in one single model. Extensive experiments
demonstrate that our MOWA, which is trained on six tasks for multiple-in-one
image warping, outperforms state-of-the-art task-specific models across most
tasks. Moreover, MOWA also exhibits promising potential to generalize into
unseen scenes, as evidenced by cross-domain and zero-shot evaluations. The code
will be made publicly available.