DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models

• URL: http://arxiv.org/abs/2603.23499v1
• Date: Tue, 24 Mar 2026 17:59:13 GMT
• Title: DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models
• Abstract summary: De-Aware Optical Flow is a new task targeting accurate dense correspondence estimation from real-world corrupted videos.<n>Our key insight is that the intermediate of image restoration diffusion models are inherently corruption-aware but lack temporal awareness.<n>We present DA-Flow, a hybrid architecture that fuses these diffusion features with convolutional features within an iterative refinement framework.
• Score: 32.839049490986554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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