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Related Experiment Video

Updated: Sep 19, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Flow-Anything: Learning Real-World Optical Flow Estimation From Large-Scale Single-View Images.

Yingping Liang, Ying Fu, Yutao Hu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 16, 2025
    PubMed
    Summary

    This study introduces Flow-Anything, a framework for generating optical flow training data from real-world images. It overcomes domain gaps, improving computer vision tasks and outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Optical flow estimation is vital for video analysis but hindered by synthetic training data.
    • Current methods face domain gaps when applied to real-world scenarios.

    Purpose of the Study:

    • To develop a large-scale data generation framework, Flow-Anything, for optical flow estimation.
    • To learn optical flow from real-world single-view images, bridging the gap between synthetic and real data.

    Main Methods:

    • Converting single-view images into 3D representations using monocular depth estimation.
    • Employing Object-Independent Volume Rendering and Depth-Aware Inpainting for dynamic object modeling.
    • Generating the FA-Flow Dataset from large-scale real-world images.

    Main Results:

    • Demonstrated the efficacy of generating optical flow training data from real-world images.
    • Achieved superior performance compared to state-of-the-art unsupervised and synthetic supervised methods.
    • Showcased Flow-Anything as a foundation model enhancing downstream video tasks.

    Conclusions:

    • Flow-Anything effectively generates realistic optical flow data from real-world images.
    • The generated dataset significantly improves optical flow estimation robustness and performance.
    • This approach offers a scalable solution for training robust computer vision models.