FlowPainter：通过置信度引导完成来修复光流

中文摘要 AI 辅助

现有的光流方法主要遵循两种范式：迭代优化和基于扩散的估计。迭代方法以RAFT为例，通过循环细化实现高精度，但在大位移和复杂运动方面仍面临挑战。基于扩散的方法引入了生成建模，在模糊区域显示出前景。然而，现有的扩散模型通常从高斯噪声中对整个密集流场进行去噪，包括简单区域，这增加了去噪负担，可能导致收敛缓慢和训练不稳定。为解决此问题，我们引入FlowPainter，一种基于扩散的光流框架，将密集流生成重新表述为置信度引导的软修复。FlowPainter使用轻量级置信度感知网络预测粗略流和逐像素置信度掩码，区分可靠的简单区域和不确定的困难区域。得到的简单流先验用于基于置信度的初始化，并通过置信度门控残差引导进一步注入迭代去噪。通过动态衰减引导强度，FlowPainter稳定早期去噪，同时保留扩散模型后期细节细化的灵活性。在包括Sintel、KITTI和Spring等公共基准上的广泛实验表明，FlowPainter在可比训练设置下实现了高精度，比现有的基于扩散的光流方法收敛更有效，在具有挑战性的基准分割上有显著提升。我们的方法提供了一种将可靠的判别先验与基于扩散的细化相结合的实用方法来进行光流估计。我们的代码可在该https URL公开获取。

英文摘要

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.

发表机构

• Nankai University（南开大学）
• The Hong Kong Polytechnic University（香港理工大学）
• OPPO Research Institute（OPPO研究院）

机构由 AI 辅助整理，请以论文原文为准。