HMC: Hierarchical Mesh Coarsening for Skeleton-free Motion Retargeting
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 20 Mar 2023.
Builds on
15 of 40 references.
References · 15
- 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,208
- 2020Skeleton-Aware Networks for Deep Motion Retargetingcited by 118
- 2021SimPoE: Simulated Character Control for 3D Human Pose Estimationcited by 111
- 2018Neural Kinematic Networks for Unsupervised Motion Retargettingcited by 85
- 2020Unsupervised Shape and Pose Disentanglement for 3D Meshescited by 61
- 2018Photo Wake-Up: 3D Character Animation from a Single Photocited by 60
- 2021Learning Skeletal Articulations with Neural Blend Shapescited by 52
- 2020Neural Pose Transfer by Spatially Adaptive Instance Normalizationcited by 37
- 2021Contact-Aware Retargeting of Skinned Motioncited by 31
- 20213D Pose Transfer with Correspondence Learning and Mesh Refinementcited by 30
- 2022Skeleton-free Pose Transfer for Stylized 3D Characterscited by 29
- 2021Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfercited by 20
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2023OmniMotionGPT: Animal Motion Generation with Limited Datacited by 18
- 2023TapMo: Shape-aware Motion Generation of Skeleton-free Characterscited by 15
- 2024Towards High-Quality 3D Motion Transfer with Realistic Apparel Animationcited by 5
- 2026AniGen: Unified S3 Fields for Animatable 3D Asset Generationcited by 3
- 2025ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Priorcited by 2
- 2026AniGen: Unified S3 Fields for Animatable 3D Asset Generation9 Apr 2026
- 2025ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior17 Mar 2025
- 2024Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation15 Jul 2024
- 2023OmniMotionGPT: Animal Motion Generation with Limited Data30 Nov 2023
- 2023TapMo: Shape-aware Motion Generation of Skeleton-free Characters19 Oct 2023