This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras. The difficulty arises from the noisy or false 2D keypoint detections due to inter-person occlusion, the heavy ambiguity in associating keypoints to individuals due to the close interactions, and the scarcity of training data as collecting and annotating motion data in crowded scenes is resource-intensive. We introduce a novel system to address these challenges. Our system integrates a learning-based pose estimation component and its corresponding training and inference strategies. The pose estimation component takes multi-view 2D keypoint heatmaps as input and reconstructs the pose of each individual using a 3D conditional volumetric network. As the network doesn't need images as input, we can leverage known camera parameters from test scenes and a large quantity of existing motion capture data to synthesize massive training data that mimics the real data distribution in test scenes. Extensive experiments demonstrate that our approach significantly surpasses previous approaches in terms of pose accuracy and is generalizable across various camera setups and population sizes. The code is available on our project page: https://github.com/zju3dv/CloseMoCap.

该研究论文讨论了通过多个校准相机捕捉的近距离互动中多个个体的姿势重建的挑战性任务，并提出了一种整合了学习为基础的姿势估计组件和相应的训练和推断策略的系统。该系统利用多视图2D关键点热图作为输入，使用3D条件体积网络重建每个个体的姿势，通过合成大规模的模拟测试场景中真实数据分布的训练数据集，实现了对姿势准确性的显著提升，并且在不同相机配置和人群规模下具有很好的泛化性能。

从多个视角重建近距离人类互动