Estimating 2D Camera Motion with Hybrid Motion Basis

• URL: http://arxiv.org/abs/2507.22480v1
• Date: Wed, 30 Jul 2025 08:30:37 GMT
• Title: Estimating 2D Camera Motion with Hybrid Motion Basis
• Authors: Haipeng Li, Tianhao Zhou, Zhanglei Yang, Yi Wu, Yan Chen, Zijing Mao, Shen Cheng, Bing Zeng, Shuaicheng Liu,
• Abstract summary: CamFlow is a novel framework that represents camera motion using hybrid motion bases.<n>Our approach includes a hybrid probabilistic loss function based on the Laplace distribution.<n>Experiments show CamFlow outperforms state-of-the-art methods across diverse scenarios.
• Score: 45.971928868591334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating 2D camera motion is a fundamental computer vision task that models the projection of 3D camera movements onto the 2D image plane. Current methods rely on either homography-based approaches, limited to planar scenes, or meshflow techniques that use grid-based local homographies but struggle with complex non-linear transformations. A key insight of our work is that combining flow fields from different homographies creates motion patterns that cannot be represented by any single homography. We introduce CamFlow, a novel framework that represents camera motion using hybrid motion bases: physical bases derived from camera geometry and stochastic bases for complex scenarios. Our approach includes a hybrid probabilistic loss function based on the Laplace distribution that enhances training robustness. For evaluation, we create a new benchmark by masking dynamic objects in existing optical flow datasets to isolate pure camera motion. Experiments show CamFlow outperforms state-of-the-art methods across diverse scenarios, demonstrating superior robustness and generalization in zero-shot settings. Code and datasets are available at our project page: https://lhaippp.github.io/CamFlow/.

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