FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion

• URL: http://arxiv.org/abs/2607.10140v1
• Date: Sat, 11 Jul 2026 05:59:22 GMT
• Title: FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion
• Abstract summary: FlowPainter is a diffusion-based optical flow framework that reformulates dense-flow generation as confidence-guided soft inpainting.<n>Our approach offers a practical way to integrate reliable discriminative priors with diffusion-based refinement for optical flow estimation.
• Score: 52.3830227666301
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• BokehDiff: Neural Lens Blur with One-Step Diffusion [62.59018200914645]
  We introduce BokehDiff, a lens blur rendering method that achieves physically accurate and visually appealing outcomes.<n>Our method employs a physics-inspired self-attention module that aligns with the image formation process.<n>We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity.
  arXiv  Detail & Related papers  (2025-07-24T03:23:19Z)
• Diffusion prior as a direct regularization term for FWI [0.0]
  We propose a score-based generative diffusion prior into Full Waveform Inversion (FWI)<n>Unlike traditional diffusion approaches, our method avoids the reverse diffusion sampling and needs fewer iterations.<n>The proposed method offers enhanced fidelity and robustness compared to conventional and GAN-based FWI approaches.
  arXiv  Detail & Related papers  (2025-06-11T19:43:23Z)
• Rectified Flows for Fast Multiscale Fluid Flow Modeling [11.597597438962026]
  We introduce a rectified flow framework that learns a time-dependent velocity field.<n>Our method makes each integration step much more effective, using as few as eight steps.<n> Experiments on challenging multiscale flow benchmarks show that rectified flows recover the same posterior distributions as diffusion models.
  arXiv  Detail & Related papers  (2025-06-03T17:40:39Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Coherent Video Inpainting Using Optical Flow-Guided Efficient Diffusion [15.188335671278024]
  We propose a new video inpainting framework using optical Flow-guided Efficient Diffusion (FloED) for higher video coherence.<n>FloED employs a dual-branch architecture, where the time-agnostic flow branch restores corrupted flow first, and the multi-scale flow adapters provide motion guidance to the main inpainting branch.<n>Experiments on background restoration and object removal tasks show that FloED outperforms state-of-the-art diffusion-based methods in both quality and efficiency.
  arXiv  Detail & Related papers  (2024-12-01T15:45:26Z)
• FlowIE: Efficient Image Enhancement via Rectified Flow [71.6345505427213]
  FlowIE is a flow-based framework that estimates straight-line paths from an elementary distribution to high-quality images. Our contributions are rigorously validated through comprehensive experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-06-01T17:29:29Z)
• Multi-scale Diffusion Denoised Smoothing [79.95360025953931]
  randomized smoothing has become one of a few tangible approaches that offers adversarial robustness to models at scale. We present scalable methods to address the current trade-off between certified robustness and accuracy in denoised smoothing. Our experiments show that the proposed multi-scale smoothing scheme combined with diffusion fine-tuning enables strong certified robustness available with high noise level.
  arXiv  Detail & Related papers  (2023-10-25T17:11:21Z)
• The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation [42.48819460873482]
  Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions.
  arXiv  Detail & Related papers  (2023-06-02T21:26:20Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.