Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset

• URL: http://arxiv.org/abs/2512.10321v1
• Date: Thu, 11 Dec 2025 06:11:24 GMT
• Title: Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset
• Authors: Hyunsoo Lee, Daeum Jeon, Hyeokjae Oh,
• Abstract summary: 3D human pose estimation poses several challenges due to the complex geometry of the human body and self-cluding joints.<n>We introduce a framework that effectively conditioned the distribution of human poses and pose history.<n>We present a large-scale indoor dataset MVPose3D, which contains multiple modalities.
• Score: 6.181093777643576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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