GenOpticalFlow: A Generative Approach to Unsupervised Optical Flow Learning

• URL: http://arxiv.org/abs/2603.22270v1
• Date: Mon, 23 Mar 2026 17:55:52 GMT
• Title: GenOpticalFlow: A Generative Approach to Unsupervised Optical Flow Learning
• Abstract summary: textbfmodelname is a novel framework that synthesizes perfectly aligned frame--flow data pairs for supervised optical flow training without human annotations.<n>We propose an textitinconsistent pixel filtering strategy that identifies and removes unreliable pixels in generated frames.
• Score: 22.637624591116417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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