# Skinned Motion Retargeting with Dense Geometric Interaction Perception

arXiv: https://arxiv.org/abs/2410.20986 · HTML: https://mlsanity.com/paper/2410.20986/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 28 Oct 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [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)
- [FaceFormer: Speech-Driven 3D Facial Animation with Transformers](https://mlsanity.com/paper/2112.05329/) (2021)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018)
- [TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement](https://mlsanity.com/paper/2205.07982/) (2022)
- [Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry](https://mlsanity.com/paper/2303.08658/) (2023)
- [Contact-Aware Retargeting of Skinned Motion](https://mlsanity.com/paper/2109.07431/) (2021)
- [A Survey on Deep Learning for Skeleton-Based Human Animation](https://mlsanity.com/paper/2110.06901/) (2021)
- [Semantics-aware Motion Retargeting with Vision-Language Models](https://mlsanity.com/paper/2312.01964/) (2023)

## Built on by (most cited first, 13 of 13)

- [AnyTop: Character Animation Diffusion with Any Topology](https://mlsanity.com/paper/2502.17327/) (2025)
- [Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence](https://mlsanity.com/paper/2508.13139/) (2025)
- [Auto-Connect: Connectivity-Preserving RigFormer with Direct Preference Optimization](https://mlsanity.com/paper/2506.11430/) (2025)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [PALUM: Part-based Attention Learning for Unified Motion Retargeting](https://mlsanity.com/paper/2601.07272/) (2026)
- [ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes](https://mlsanity.com/paper/2604.17623/) (2026)
- [AnyCharV: Bootstrap Controllable Character Video Generation with Fine-to-Coarse Guidance](https://mlsanity.com/paper/2502.08189/) (2025)
- [STaR: Seamless Spatial-Temporal Aware Motion Retargeting with Penetration and Consistency Constraints](https://mlsanity.com/paper/2504.06504/) (2025)
- [Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)](https://mlsanity.com/paper/2501.08609/) (2025)
- [A Unified Conditional Flow for Motion Generation, Editing, and Intra-Structural Retargeting](https://mlsanity.com/paper/2604.13427/) (2026)
- [SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals](https://mlsanity.com/paper/2605.18039/) (2026)
- [Skinned Motion Retargeting with Spatially Adaptive Interaction Guidance](https://mlsanity.com/paper/2605.19355/) (2026)
- [ReFM: Semantic-Aware Refinement Flow Model for Motion Retargeting](https://mlsanity.com/paper/2609.32068/) (2026)

## Abstract (arXiv)

Capturing and maintaining geometric interactions among different body parts is crucial for successful motion retargeting in skinned characters. Existing approaches often overlook body geometries or add a geometry correction stage after skeletal motion retargeting. This results in conflicts between skeleton interaction and geometry correction, leading to issues such as jittery, interpenetration, and contact mismatches. To address these challenges, we introduce a new retargeting framework, MeshRet, which directly models the dense geometric interactions in motion retargeting. Initially, we establish dense mesh correspondences between characters using semantically consistent sensors (SCS), effective across diverse mesh topologies. Subsequently, we develop a novel spatio-temporal representation called the dense mesh interaction (DMI) field. This field, a collection of interacting SCS feature vectors, skillfully captures both contact and non-contact interactions between body geometries. By aligning the DMI field during retargeting, MeshRet not only preserves motion semantics but also prevents self-interpenetration and ensures contact preservation. Extensive experiments on the public Mixamo dataset and our newly-collected ScanRet dataset demonstrate that MeshRet achieves state-of-the-art performance. Code available at https://github.com/abcyzj/MeshRet.