Optical flow models trained on high-quality data often degrade severely when confronted with real-world corruptions such as blur, noise, and compression artifacts. To overcome this limitation, we formulate Degradation-Aware Optical Flow, a new task targeting accurate dense correspondence estimation from real-world corrupted videos. Our key insight is that the intermediate representations of image restoration diffusion models are inherently corruption-aware but lack temporal awareness. To address this limitation, we lift the model to attend across adjacent frames via full spatio-temporal attention, and empirically demonstrate that the resulting features exhibit zero-shot correspondence capabilities. Based on this finding, we present DA-Flow, a hybrid architecture that fuses these diffusion features with convolutional features within an iterative refinement framework. DA-Flow substantially outperforms existing optical flow methods under severe degradation across multiple benchmarks.

翻译：针对高质量数据训练的光流模型在面对真实世界中的模糊、噪声和压缩伪影等退化问题时，性能往往显著下降。为解决这一局限，本文提出了退化感知光流这一新任务，旨在从真实世界退化视频中实现精确的密集对应估计。我们的核心洞察在于：图像复原扩散模型的中间表征天然具有退化感知能力，但缺乏时序感知特性。为此，我们通过全时空注意力机制提升模型对相邻帧的跨帧关注能力，并实验证明由此获得的特征具有零样本对应估计能力。基于这一发现，我们提出DA-Flow——一种在迭代优化框架中融合扩散特征与卷积特征的混合架构。在多个基准测试中，DA-Flow在严重退化条件下显著优于现有光流方法。