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

We propose a novel generative approach for 3D human pose estimation. 3D human pose estimation poses several key challenges due to the human body's complex geometry, self-occluding joints, and the requirement for large-scale real-world motion datasets. To address these challenges, we introduce Point2Pose, a novel generative framework that effectively models the distribution of human poses conditioned on sequential point cloud and pose data. 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 baseline models, demonstrating its superior performance across various datasets.