# A Kinematic Chain Space for Monocular Motion Capture

## TL;DR

This paper introduces a novel monocular motion capture method using a kinematic chain space that does not rely on training data or specific camera/object motion, enabling reconstruction of diverse kinematic chains.

## Contribution

The method projects observations into a kinematic chain space and employs nuclear norm optimization to enforce structural properties without requiring prior constraints or calibration.

## Key findings

- Achieves state-of-the-art results on benchmark datasets.
- Works with limited camera motion and unseen motions.
- Applicable to various kinematic chains beyond human skeletons.

## Abstract

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.