# Redefining Data Pairing for Motion Retargeting Leveraging a Human Body Prior

arXiv: https://arxiv.org/abs/2409.13208 · HTML: https://mlsanity.com/paper/2409.13208/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018)
- [Whole-Body Geometric Retargeting for Humanoid Robots](https://mlsanity.com/paper/1909.10080/) (2019)
- [MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks](https://mlsanity.com/paper/2112.10082/) (2021)
- [ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space](https://mlsanity.com/paper/2309.05310/) (2023)
- [Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language](https://mlsanity.com/paper/2103.08882/) (2021)
- [Self-Supervised Motion Retargeting with Safety Guarantee](https://mlsanity.com/paper/2103.06447/) (2021)

## Abstract (arXiv)

We propose MR HuBo(Motion Retargeting leveraging a HUman BOdy prior), a cost-effective and convenient method to collect high-quality upper body paired <robot, human> pose data, which is essential for data-driven motion retargeting methods. Unlike existing approaches which collect <robot, human> pose data by converting human MoCap poses into robot poses, our method goes in reverse. We first sample diverse random robot poses, and then convert them into human poses. However, since random robot poses can result in extreme and infeasible human poses, we propose an additional technique to sort out extreme poses by exploiting a human body prior trained from a large amount of human pose data. Our data collection method can be used for any humanoid robots, if one designs or optimizes the system's hyperparameters which include a size scale factor and the joint angle ranges for sampling. In addition to this data collection method, we also present a two-stage motion retargeting neural network that can be trained via supervised learning on a large amount of paired data. Compared to other learning-based methods trained via unsupervised learning, we found that our deep neural network trained with ample high-quality paired data achieved notable performance. Our experiments also show that our data filtering method yields better retargeting results than training the model with raw and noisy data. Our code and video results are available on https://sites.google.com/view/mr-hubo/