FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion

Existing optical flow estimation methods broadly follow twoparadigms: iterative optimization and diffusion-based estimation. Iter-ative methods, exemplified by RAFT, achieve accurate flow estimationthrough recurrent refinement, but can still be challenged by large displace-ments and complex motion patterns. Diffusion-based methods introducegenerative modeling into optical flow and have shown promising resultsin such ambiguous regions. However, existing diffusion-based flow modelsusually denoise the entire dense flow field from Gaussian noise, includingsimple regions where reliable motion structure can already be estimatedby a lightweight network. This increases the denoising burden and maylead to slow convergence and unstable training. To address these issues,we introduce FlowPainter, a diffusion-based optical flow framework thatreformulates dense-flow generation as confidence-guided soft inpainting.FlowPainter first employs a lightweight confidence-aware network to pre-dict a rough flow and a pixel-wise confidence mask, which serves as areliability gate for distinguishing reliable simple regions from uncertainhard regions. The resulting simple-flow prior is used for confidence-basedinitialization and is further injected into the iterative denoising processthrough confidence-gated residual guidance. With a dynamically decayingguidance strength, FlowPainter stabilizes early denoising while preservingthe flexibility of the diffusion model for late-stage detail refinement. Ex-tensive experiments on public benchmarks, including Sintel, KITTI, andSpring, demonstrate that FlowPainter achieves strong accuracy undercomparable training settings and improves convergence efficiency overexisting diffusion-based optical flow methods, with notable gains on chal-lenging benchmark splits. Our approach provides a practical direction forintegrating reliable discriminative priors with diffusion-based refinementfor optical flow estimation.