FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion
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 Flo...
| Authors | Yuang Meng, Chenyang Wu, Xianshun Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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