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.

翻译：光流估计是计算机视觉中的一个基本问题，然而对昂贵的真实标注数据的依赖限制了有监督方法的可扩展性。尽管无监督和半监督方法缓解了这个问题，但它们常常受限于基于亮度恒常性和平滑性假设的不可靠监督信号，从而导致在复杂真实场景中的运动估计不准确。为了克服这些限制，我们引入了 \textbf{\modelname}，这是一个新颖的框架，它无需人工标注即可合成大规模、完美对齐的帧-流数据对，用于有监督的光流训练。具体而言，我们的方法利用一个预训练的深度估计网络来生成伪光流，这些伪光流作为一个下一帧生成模型的条件输入，该模型被训练用于生成高保真、像素对齐的后续帧。这一过程使得能够创建大量且具有精确运动对应关系的高质量合成数据。此外，我们提出了一种*不一致像素过滤*策略，用于识别并移除生成帧中不可靠的像素，从而有效提升在真实世界数据集上的微调性能。在KITTI2012、KITTI2015和Sintel上的大量实验表明，与现有的无监督和半监督方法相比，\textbf{\modelname} 取得了具有竞争力甚至更优的结果，突显了其作为光流学习的一种可扩展且无需标注的解决方案的潜力。我们将在论文被接收后公开代码。