用点云生成3D人体姿态，解决遮挡与数据不足难题
Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset
- 通过时空点云编码器+注意力生成回归器建模姿态分布
- 在MVPose3D等数据集上显著优于基线模型
- 适合做多视角点云姿态估计的科研与工业应用
我们提出一种新型生成式3D人体姿态估计方法。由于人体结构复杂、关节自遮挡及真实世界运动数据规模不足，3D人体姿态估计面临诸多挑战。为此，我们引入Point2Pose框架，通过序列点云和姿态历史条件建模人体姿态分布。具体地，采用时空点云编码器与姿态特征编码器提取关节点特征，再通过基于注意力的生成回归器输出姿态。此外，我们构建了大规模室内多模态数据集MVPose3D，包含非平凡人体动作的IMU数据、密集多视角点云及RGB图像。实验表明，所提方法在多个数据集上均优于基线模型，性能更优。
原文摘要 · Abstract (English)
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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