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.

本研究提出了一种名为 MOWA 的多合一图像变形模型，通过在区域级和像素级上分离运动估计，以及引入轻量级基于点的分类器，为不同的实际变形任务提供动态的任务感知图像变形。实验证明，我们的 MOWA 模型在六个任务的多合一图像变形训练中，表现优于最先进的任务特定模型，并且还展现了潜在的泛化能力。该研究的代码将公开可用。