Optical flow estimation — computing pixel-level motion between video frames — works well on clean data and poorly on degraded data. Blur, noise, and compression artifacts confuse the appearance-matching that flow algorithms rely on.
The authors (arXiv:2603.23499) discover that image restoration diffusion models, which already understand corruption types, naturally capture temporal correspondence when extended to adjacent frames. Adding spatio-temporal attention to a diffusion denoiser produces features that encode both corruption-awareness and motion information.
DA-Flow merges these diffusion-based features with traditional convolutional features through iterative refinement. The convolutional features provide fine spatial detail; the diffusion features provide corruption-invariant matching. The hybrid substantially outperforms existing flow methods under severe degradation.
The through-claim: the degradation model and the motion model are not independent problems — they share latent structure. A diffusion model trained to remove blur already knows that the blurred appearance of a moving edge differs from a stationary edge. This implicit motion knowledge, extracted from the diffusion process and combined with explicit flow estimation, produces robust correspondence that neither approach achieves alone.