# Markerless Body Motion Capturing for 3D Character Animation based on Multi-view Cameras

arXiv: https://arxiv.org/abs/2212.05788 · HTML: https://mlsanity.com/paper/2212.05788/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Harvesting Multiple Views for Marker-less 3D Human Pose Annotations](https://mlsanity.com/paper/1704.04793/) (2017)
- [FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras](https://mlsanity.com/paper/1610.09534/) (2016)
- [General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues](https://mlsanity.com/paper/1607.08659/) (2016)
- [A Bayesian Framework for Sparse Representation-Based 3D Human Pose Estimation](https://mlsanity.com/paper/1412.0062/) (2014)

## Built on by (most cited first, 1 of 1)

- [Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation](https://mlsanity.com/paper/2403.06164/) (2024)

## Abstract (arXiv)

This paper proposes a novel application system for the generation of three-dimensional (3D) character animation driven by markerless human body motion capturing. The entire pipeline of the system consists of five stages: 1) the capturing of motion data using multiple cameras, 2) detection of the two-dimensional (2D) human body joints, 3) estimation of the 3D joints, 4) calculation of bone transformation matrices, and 5) generation of character animation. The main objective of this study is to generate a 3D skeleton and animation for 3D characters using multi-view images captured by ordinary cameras. The computational complexity of the 3D skeleton reconstruction based on 3D vision has been reduced as needed to achieve frame-by-frame motion capturing. The experimental results reveal that our system can effectively and efficiently capture human actions and use them to animate 3D cartoon characters in real-time.