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

2026-03-24

AI Summary

Existing optical flow methods suffer significant performance degradation under realistic image degradations such as motion blur, noise, and compression artifacts. This work proposes a hybrid architecture that integrates intermediate features from diffusion models with convolutional representations, leveraging—for the first time—the inherent degradation-aware capabilities of diffusion models for optical flow estimation. By introducing a cross-frame spatiotemporal attention mechanism, the method enables zero-shot correspondence modeling without requiring retraining or fine-tuning under diverse degradations. The resulting framework establishes a new paradigm for degradation-robust optical flow estimation, consistently outperforming current state-of-the-art approaches across multiple benchmarks under various severe degradation conditions.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• optical flow
• image degradation
• real-world corruptions
• dense correspondence
• video restoration

Innovation

• Degradation-Aware Optical Flow
• Diffusion Models
• Spatio-Temporal Attention
• Zero-Shot Correspondence
• Iterative Refinement