GenOpticalFlow: A Generative Approach to Unsupervised Optical Flow Learning
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 ...
| Authors | Yixuan Luo, Feng Qiao, Zhexiao Xiong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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