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FlowPainter employs a lightweight confidence-aware network to predict a rough flow and a pixel-wise confidence mask, distin","title":"FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion","url":"https://arxiv.org/abs/2607.10140","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.10140v1 Announce Type: cross \nAbstract: 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. 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