Abstract
Optical flow estimation is a fundamental problem in computer vision, yet the reliance on expensive ground-truth annotations limits the scalability of supervised approaches. Although unsupervised and semi-supervised methods alleviate this issue, they often suffer from unreliable supervision signals based on brightness constancy and smoothness assumptions, leading to inaccurate motion estimation in complex real-world scenarios. To overcome these limitations, we introduce \textbf{\modelname}, a novel framework that synthesizes large-scale, perfectly aligned frame--flow data pairs for supervised optical flow training without human annotations. Specifically, our method leverages a pre-trained depth estimation network to generate pseudo optical flows, which serve as conditioning inputs for a next-frame generation model trained to produce high-fidelity, pixel-aligned subsequent frames. This process enables the creation of abundant, high-quality synthetic data with precise motion correspondence. Furthermore, we propose an \textit{inconsistent pixel filtering} strategy that identifies and removes unreliable pixels in generated frames, effectively enhancing fine-tuning performance on real-world datasets. Extensive experiments on KITTI2012, KITTI2015, and Sintel demonstrate that \textbf{\modelname} achieves competitive or superior results compared to existing unsupervised and semi-supervised approaches, highlighting its potential as a scalable and annotation-free solution for optical flow learning. We will release our code upon acceptance.
Abstract (translated)
光流估计是计算机视觉中的基础问题，但依赖昂贵的真值标注限制了监督方法的可扩展性。尽管无监督与半监督方法缓解了这一问题，它们常因基于亮度恒定和平滑性假设的不可靠监督信号，在复杂真实场景中产生不准确的运动估计。为克服这些限制，我们提出\textbf{\modelname}，一种无需人工标注即可为监督光流训练合成大规模、精确对齐的帧-光流数据对的新框架。具体而言，我们的方法利用预训练深度估计网络生成伪光流，作为下一帧生成模型的输入条件，该模型经训练可生成高保真、像素对齐的后续帧。此过程能创建具有精确运动对应关系的丰富高质量合成数据。此外，我们提出\textit{不一致像素过滤}策略，识别并移除生成帧中的不可靠像素，有效提升在真实数据集上的微调性能。在KITTI2012、KITTI2015和Sintel上的大量实验表明，\textbf{\modelname}相比现有无监督与半监督方法取得了具有竞争力或更优的结果，凸显其作为可扩展、无标注解决方案的光流学习潜力。代码将在论文接收后公开。
URL
https://arxiv.org/abs/2603.22270
