This paper deals with motion capture of kinematic chains (e.g. human
skeletons) from monocular image sequences taken by uncalibrated cameras. We
present a method based on projecting an observation into a kinematic chain
space (KCS). An optimization of the nuclear norm is proposed that implicitly
enforces structural properties of the kinematic chain. Unlike other approaches
our method does not require specific camera or object motion and is not relying
on training data or previously determined constraints such as particular body
lengths. The proposed algorithm is able to reconstruct scenes with limited
camera motion and previously unseen motions. It is not only applicable to human
skeletons but also to other kinematic chains for instance animals or industrial
robots. We achieve state-of-the-art results on different benchmark data bases
and real world scenes.

本文提出了一种基于核范数优化的运动捕捉方法，该方法不需要特定的相机或物体运动，并且不依赖于训练数据或先前确定的约束条件，能够重建具有有限相机运动和先前未见过的运动的情景，适用于人体骨架、动物或工业机器人等其他运动链