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arXiv · 2512.10321

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

Hyunsoo Lee·Daeum Jeon·Hyeokjae Oh
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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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Hyunsoo Lee, Daeum Jeon, Hyeokjae Oh. 2025-12-11. Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset. https://arxiv.org/abs/2512.10321

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