# HMC: Hierarchical Mesh Coarsening for Skeleton-free Motion Retargeting

arXiv: https://arxiv.org/abs/2303.10941 · HTML: https://mlsanity.com/paper/2303.10941/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [SimPoE: Simulated Character Control for 3D Human Pose Estimation](https://mlsanity.com/paper/2104.00683/) (2021)
- [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018)
- [Unsupervised Shape and Pose Disentanglement for 3D Meshes](https://mlsanity.com/paper/2007.11341/) (2020)
- [Photo Wake-Up: 3D Character Animation from a Single Photo](https://mlsanity.com/paper/1812.02246/) (2018)
- [Learning Skeletal Articulations with Neural Blend Shapes](https://mlsanity.com/paper/2105.02451/) (2021)
- [Neural Pose Transfer by Spatially Adaptive Instance Normalization](https://mlsanity.com/paper/2003.07254/) (2020)
- [Contact-Aware Retargeting of Skinned Motion](https://mlsanity.com/paper/2109.07431/) (2021)
- [3D Pose Transfer with Correspondence Learning and Mesh Refinement](https://mlsanity.com/paper/2109.15025/) (2021)
- [Skeleton-free Pose Transfer for Stylized 3D Characters](https://mlsanity.com/paper/2208.00790/) (2022)
- [Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer](https://mlsanity.com/paper/2108.07520/) (2021)
- [SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters](https://mlsanity.com/paper/2203.04746/) (2022)
- [MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks](https://mlsanity.com/paper/2112.10082/) (2021)
- [Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders](https://mlsanity.com/paper/2012.02459/) (2020)

## Built on by (most cited first, 6 of 6)

- [OmniMotionGPT: Animal Motion Generation with Limited Data](https://mlsanity.com/paper/2311.18303/) (2023)
- [TapMo: Shape-aware Motion Generation of Skeleton-free Characters](https://mlsanity.com/paper/2310.12678/) (2023)
- [Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation](https://mlsanity.com/paper/2407.11266/) (2024)
- [AniGen: Unified S3 Fields for Animatable 3D Asset Generation](https://mlsanity.com/paper/2604.08746/) (2026)
- [ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior](https://mlsanity.com/paper/2503.13579/) (2025)
- [Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion](https://mlsanity.com/paper/2610.18871/) (2026)

## Abstract (arXiv)

We present a simple yet effective method for skeleton-free motion retargeting. Previous methods transfer motion between high-resolution meshes, failing to preserve the inherent local-part motions in the mesh. Addressing this issue, our proposed method learns the correspondence in a coarse-to-fine fashion by integrating the retargeting process with a mesh-coarsening pipeline. First, we propose a mesh-coarsening module that coarsens the mesh representations for better motion transfer. This module improves the ability to handle small-part motion and preserves the local motion interdependence between neighboring mesh vertices. Furthermore, we leverage a hierarchical refinement procedure to complement missing mesh details by gradually improving the low-resolution mesh output with a higher-resolution one. We evaluate our method on several well-known 3D character datasets, and it yields an average improvement of 25% on point-wise mesh euclidean distance (PMD) against the start-of-art method. Moreover, our qualitative results show that our method is significantly helpful in preserving the moving consistency of different body parts on the target character due to disentangling body-part structures and mesh details in a hierarchical way.