Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images

• URL: http://arxiv.org/abs/2506.07740v1
• Date: Mon, 09 Jun 2025 13:23:44 GMT
• Title: Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images
• Authors: Yingping Liang, Ying Fu, Yutao Hu, Wenqi Shao, Jiaming Liu, Debing Zhang,
• Abstract summary: We develop a large-scale data generation framework designed to learn optical flow estimation from any single-view images in the real world.<n>For the first time, we demonstrate the benefits of generating optical flow training data from large-scale real-world images.<n>Our models serve as a foundation model and enhance the performance of various downstream video tasks.
• Score: 23.731451842621933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical flow estimation is a crucial subfield of computer vision, serving as a foundation for video tasks. However, the real-world robustness is limited by animated synthetic datasets for training. This introduces domain gaps when applied to real-world applications and limits the benefits of scaling up datasets. To address these challenges, we propose \textbf{Flow-Anything}, a large-scale data generation framework designed to learn optical flow estimation from any single-view images in the real world. We employ two effective steps to make data scaling-up promising. First, we convert a single-view image into a 3D representation using advanced monocular depth estimation networks. This allows us to render optical flow and novel view images under a virtual camera. Second, we develop an Object-Independent Volume Rendering module and a Depth-Aware Inpainting module to model the dynamic objects in the 3D representation. These two steps allow us to generate realistic datasets for training from large-scale single-view images, namely \textbf{FA-Flow Dataset}. For the first time, we demonstrate the benefits of generating optical flow training data from large-scale real-world images, outperforming the most advanced unsupervised methods and supervised methods on synthetic datasets. Moreover, our models serve as a foundation model and enhance the performance of various downstream video tasks.

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