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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.10321 |
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| _version_ | 1866908704992919552 |
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| author | Lee, Hyunsoo Jeon, Daeum Oh, Hyeokjae |
| author_facet | Lee, Hyunsoo Jeon, Daeum Oh, Hyeokjae |
| contents | We propose a novel generative approach for 3D human pose estimation. 3D human pose estimation poses several key challenges due to the complex geometry of the human body, self-occluding joints, and the requirement for large-scale real-world motion datasets. To address these challenges, we introduce Point2Pose, a framework that effectively models the distribution of human poses conditioned on sequential point cloud and pose history. Specifically, we employ a spatio-temporal point cloud encoder and a pose feature encoder to extract joint-wise features, followed by an attention-based generative regressor. Additionally, we present a large-scale indoor dataset MVPose3D, which contains multiple modalities, including IMU data of non-trivial human motions, dense multi-view point clouds, and RGB images. Experimental results show that the proposed method outperforms the baseline models, demonstrating its superior performance across various datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset Lee, Hyunsoo Jeon, Daeum Oh, Hyeokjae Computer Vision and Pattern Recognition We propose a novel generative approach for 3D human pose estimation. 3D human pose estimation poses several key challenges due to the complex geometry of the human body, self-occluding joints, and the requirement for large-scale real-world motion datasets. To address these challenges, we introduce Point2Pose, a framework that effectively models the distribution of human poses conditioned on sequential point cloud and pose history. Specifically, we employ a spatio-temporal point cloud encoder and a pose feature encoder to extract joint-wise features, followed by an attention-based generative regressor. Additionally, we present a large-scale indoor dataset MVPose3D, which contains multiple modalities, including IMU data of non-trivial human motions, dense multi-view point clouds, and RGB images. Experimental results show that the proposed method outperforms the baseline models, demonstrating its superior performance across various datasets. |
| title | Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.10321 |