DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910069720875008 |
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| author | Min, Jaewon Lee, Jaeeun Choi, Yeji Cho, Paul Hyunbin Kim, Jin Hyeon Lee, Tae-Young Ahn, Jongsik Lee, Hwayeong Park, Seonghyun Kim, Seungryong |
| author_facet | Min, Jaewon Lee, Jaeeun Choi, Yeji Cho, Paul Hyunbin Kim, Jin Hyeon Lee, Tae-Young Ahn, Jongsik Lee, Hwayeong Park, Seonghyun Kim, Seungryong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23499 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models Min, Jaewon Lee, Jaeeun Choi, Yeji Cho, Paul Hyunbin Kim, Jin Hyeon Lee, Tae-Young Ahn, Jongsik Lee, Hwayeong Park, Seonghyun Kim, Seungryong Computer Vision and Pattern Recognition 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. |
| title | DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.23499 |