FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929705212444672 |
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| author | Xue, Aoru Ren, Yiming Song, Zining Ye, Mao Zhu, Xinge Ma, Yuexin |
| author_facet | Xue, Aoru Ren, Yiming Song, Zining Ye, Mao Zhu, Xinge Ma, Yuexin |
| contents | We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable moving cameras, allowing for flexible and precise motion estimation in a unified world coordinate. In particular, We introduce a local-to-global pose-aware cross-sensor human-matching module that predicts the alignment among each sensor, even in the absence of calibration. Additionally, our coarse-to-fine sensor-expandable pose optimizer further optimizes the 3D human key points and the alignments, it is also capable of incorporating additional cameras to enhance accuracy. Extensive experiments on Human-M3 and FreeMotion datasets demonstrate that our method significantly outperforms state-of-the-art single-modal methods, offering an expandable and efficient solution for multi-person motion capture across various applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_04469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments Xue, Aoru Ren, Yiming Song, Zining Ye, Mao Zhu, Xinge Ma, Yuexin Computer Vision and Pattern Recognition We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable moving cameras, allowing for flexible and precise motion estimation in a unified world coordinate. In particular, We introduce a local-to-global pose-aware cross-sensor human-matching module that predicts the alignment among each sensor, even in the absence of calibration. Additionally, our coarse-to-fine sensor-expandable pose optimizer further optimizes the 3D human key points and the alignments, it is also capable of incorporating additional cameras to enhance accuracy. Extensive experiments on Human-M3 and FreeMotion datasets demonstrate that our method significantly outperforms state-of-the-art single-modal methods, offering an expandable and efficient solution for multi-person motion capture across various applications. |
| title | FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.04469 |