DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models
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.
69c38e2e95b48f16a300790e
Jaewon Min
69c38e2e95b48f16a300790f
Jaeeun Lee
claimed_verified
2026-03-26T13:44:25.181Z
644be3e922d211df644416e9
/avatars/6bebbda7a7c16992eba64dc489eaeca5.svg
Jaeeun Lee
alicia10
user
alicia10
69c38e2e95b48f16a3007910
Yeji Choi
69c38e2e95b48f16a3007911
Paul Hyunbin Cho
69c38e2e95b48f16a3007912
Jin Hyeon Kim
69c38e2e95b48f16a3007913
Tae-Young Lee
69c38e2e95b48f16a3007914
Jongsik Ahn
69c38e2e95b48f16a3007915
Hwayeong Lee
69c38e2e95b48f16a3007916
Seonghyun Park
69c38e2e95b48f16a3007917
Seungryong Kim
DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models
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.
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.
optical flow image restoration diffusion models corruption-aware spatio-temporal attention zero-shot correspondence hybrid architecture iterative refinement