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Summary
The paper proposes a framework for simultaneously recovering multi-person meshes and multi-view cameras with human semantics. It introduces a pose-geometry consistency association to establish cross-view and temporal correspondences for detected human semantics and a latent motion prior to refine camera parameters and human motions.
Highlights
- The framework can recover multi-person motions and accurate camera parameters from detected human semantics without calibration tools.
- The pose-geometry consistency association reduces the influence of noises and establishes correct correspondences among different views.
- The latent motion prior provides additional knowledge in motion capture and ensures temporal coherence.
- The framework can handle sparse and noisy inputs and is robust to occlusions.
- It can be applied to various scenarios, including sports broadcasting, virtual reality, and video games.
- The framework outperforms state-of-the-art methods in multi-person mesh recovery and camera calibration.
- It can also be used for single-view cases and is more robust to noisy detections.
Key Insights
- The framework's ability to simultaneously recover multi-person meshes and camera parameters simplifies the conventional multi-person mesh recovery process, which typically requires accurate camera calibration as a prerequisite.
- The pose-geometry consistency association is a crucial component, as it allows the framework to establish correct correspondences among different views and reduce the impact of noisy detections.
- The latent motion prior plays a vital role in ensuring the temporal coherence of the recovered motions, making it suitable for applications that require smooth and realistic motion capture.
- The framework's robustness to occlusions and sparse inputs makes it applicable to real-world scenarios where these challenges are common.
- The use of human semantics for camera estimation eliminates the need for special calibration tools, making the framework more practical and flexible.
- The framework's ability to handle single-view cases and its robustness to noisy detections make it a versatile tool for various applications.
- The experimental results demonstrate the framework's superiority over state-of-the-art methods, highlighting its potential for real-world applications.
Mindmap
Citation
Huang, B., Ju, J., Shu, Y., & Wang, Y. (2024). Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics. arXiv. https://doi.org/10.48550/ARXIV.2412.18785