Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset
This work addresses 3D human pose estimation for applications like motion capture or robotics, but it appears incremental as it builds on existing generative and attention-based methods.
The paper tackles 3D human pose estimation by proposing Point2Pose, a generative framework that models human poses from sequential point clouds and pose history, and it outperforms baseline models on various datasets.
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.