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

Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation. Iterative methods, exemplified by RAFT, achieve high accuracy through recurrent refinement, but remain challenged by large displacements and complex motion. Diffusion-based methods introduce generative modeling and show promise in such ambiguous regions. However, existing diffusion models usually denoise the entire dense flow field from Gaussian noise, including simple regions where reliable motion can already be estimated by a lightweight network. This increases the denoising burden and may cause slow convergence and unstable training. To address this issue, we introduce FlowPainter, a diffusion-based optical flow framework that reformulates dense-flow generation as confidence-guided soft inpainting. FlowPainter employs a lightweight confidence-aware network to predict a rough flow and a pixel-wise confidence mask, distinguishing reliable simple regions from uncertain hard regions. The resulting simple-flow prior is used for confidence-based initialization and further injected into iterative denoising through confidence-gated residual guidance. With dynamically decaying guidance strength, FlowPainter stabilizes early denoising while preserving the flexibility of the diffusion model for late-stage detail refinement. Extensive experiments on public benchmarks, including Sintel, KITTI, and Spring, show that FlowPainter achieves strong accuracy under comparable training settings and converges more efficiently than existing diffusion-based optical flow methods, with notable gains on challenging benchmark splits. Our approach offers a practical way to integrate reliable discriminative priors with diffusion-based refinement for optical flow estimation. Our code is publicly available at https://github.com/mya012/FlowPainter.

Optical flow estimation is a fundamental task in dynamic scene understanding and computer vision. While recent deep models achieve impressive accuracy on clean benchmarks, their robustness to common visual and temporal degradations remains insufficiently explored. To address this gap, we establish a real-video-based spatiotemporal corruption benchmark suite that complements rendered and mixed-source robustness benchmarks, systematically evaluating two complementary aspects of visual perception: domain robustness and motion-distribution robustness. We construct two targeted benchmarks: KITTI Flow Corruptions (KITTI-FC) for dynamic outdoor scenes and GoPro Flow Corruptions (GoPro-FC) for diverse first-person-view complex motions. Both benchmarks use captured source data and apply 24 controlled, predominantly algorithmic corruptions encompassing digital, illumination, weather, noise, blur, and video artifacts. To quantify robustness, we use two complementary axes: the GT-free output-stability error Rs and the GT-based accuracy degradation Rd, together with their clean-error-normalized variants. We systematically evaluate 33 model variants from 17 representative optical flow methods. Our results show that, under the evaluated corruption schedules, models exhibit greater vulnerability to local structural information loss (e.g., JPEG compression and spatter) than to global perceptual distortions (e.g., fog and saturation). We further find that in-domain fine-tuning does not guarantee relative robustness and that no single current method dominates across all tested source and motion settings. Our work provides a controlled diagnostic framework for assessing optical flow robustness and developing reliable perception systems in complex environments. Code and benchmark resources are publicly available at https://github.com/ZhonghuaYi/optical_flow_robustness_benchmark.

Generative flow models offer powerful priors learned from large-scale natural images, but directly adapting them to restoration tasks such as motion deblurring causes severe fidelity degradation, as their training objective is inherently misaligned with restoration. We present DeblurFlow, a framework that resolves this misalignment by reformulating the flow trajectory itself: we replace the noise endpoint with the blur observation, which makes the underlying vector field coincide with the residual error between blur and clean images. Under this formulation, the standard flow matching loss naturally takes the form of a residual loss, allowing pretrained flow models to be optimized under restoration-aligned objectives via LoRA adaptation. This formulation further enables a dual-expert sampling strategy: a fidelity expert provides a high-fidelity initialization, e.g., PSNR 33.69 dB, and DeblurFlow enhances perceptual quality with only a marginal fidelity reduction to 33.05 dB, whereas directly applying a generative model on top of a fidelity expert decreases PSNR to 27.60 dB. To make this practical, we further introduce r-space, a latent space tailored for residual decoding rather than image reconstruction, which reduces encoder-decoder cost by up to 9$\times$over standard VAE latents. Extensive experiments on GoPro, HIDE, RealBlur, and RWBI demonstrate that DeblurFlow achieves strong restoration fidelity and perceptual realism, while remaining computationally practical.