This paper contributes a novel realtime multi-person motion capture algorithm
using multiview video inputs. Due to the heavy occlusions in each view, joint
optimization on the multiview images and multiple temporal frames is
indispensable, which brings up the essential challenge of realtime efficiency.
To this end, for the first time, we unify per-view parsing, cross-view
matching, and temporal tracking into a single optimization framework, i.e., a
4D association graph that each dimension (image space, viewpoint and time) can
be treated equally and simultaneously. To solve the 4D association graph
efficiently, we further contribute the idea of 4D limb bundle parsing based on
heuristic searching, followed with limb bundle assembling by proposing a bundle
Kruskal's algorithm. Our method enables a realtime online motion capture system
running at 30fps using 5 cameras on a 5-person scene. Benefiting from the
unified parsing, matching and tracking constraints, our method is robust to
noisy detection, and achieves high-quality online pose reconstruction quality.
The proposed method outperforms the state-of-the-art method quantitatively
without using high-level appearance information. We also contribute a multiview
video dataset synchronized with a marker-based motion capture system for
scientific evaluation.

本文介绍了一种新颖的实时多人运动捕捉算法，使用多视角视频输入。通过联合多视角图像和多个时间帧进行关节优化，解决了运动捕捉中存在的关键挑战，该方法每秒可以在 5 个人的场景中使用 5 个相机运行 30fps 的帧率。