# Correspondence-free online human motion retargeting

arXiv: https://arxiv.org/abs/2302.00556 · HTML: https://mlsanity.com/paper/2302.00556/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 51 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [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)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [BCNet: Learning Body and Cloth Shape from A Single Image](https://mlsanity.com/paper/2004.00214/) (2020)
- [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)
- [S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling](https://mlsanity.com/paper/2101.06571/) (2021)
- [LIMP: Learning Latent Shape Representations with Metric Preservation Priors](https://mlsanity.com/paper/2003.12283/) (2020)
- [Predicting 3D Human Dynamics from Video](https://mlsanity.com/paper/1908.04781/) (2019)
- [Learning Character-Agnostic Motion for Motion Retargeting in 2D](https://mlsanity.com/paper/1905.01680/) (2019)
- [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)
- [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)
- [H4D: Human 4D Modeling by Learning Neural Compositional Representation](https://mlsanity.com/paper/2203.01247/) (2022)
- [AniFormer: Data-driven 3D Animation with Transformer](https://mlsanity.com/paper/2110.10533/) (2021)
- [A structured latent space for human body motion generation](https://mlsanity.com/paper/2106.04387/) (2021)
- [Neural Human Deformation Transfer](https://mlsanity.com/paper/2109.01588/) (2021)

## Built on by (most cited first, 7 of 7)

- [4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements](https://mlsanity.com/paper/2306.07399/) (2023)
- [Temporal Residual Jacobians For Rig-free Motion Transfer](https://mlsanity.com/paper/2407.14958/) (2024)
- [SMF: Template-free and Rig-free Animation Transfer using Kinetic Codes](https://mlsanity.com/paper/2504.04831/) (2025)
- [MoReFlow: Motion Retargeting Learning through Unsupervised Flow Matching](https://mlsanity.com/paper/2509.25600/) (2025)
- [Quality assessment of 3D human animation: Subjective and objective evaluation](https://mlsanity.com/paper/2505.23301/) (2025)
- [ScanMove: Motion Prediction and Transfer for Unregistered Body Meshes](https://mlsanity.com/paper/2508.21095/) (2025)
- [EMP: Executable Motion Prior for Humanoid Robot Standing Upper-body Motion Imitation](https://mlsanity.com/paper/2507.15649/) (2025)

## Abstract (arXiv)

We present a data-driven framework for unsupervised human motion retargeting that animates a target subject with the motion of a source subject. Our method is correspondence-free, requiring neither spatial correspondences between the source and target shapes nor temporal correspondences between different frames of the source motion. This allows to animate a target shape with arbitrary sequences of humans in motion, possibly captured using 4D acquisition platforms or consumer devices. Our method unifies the advantages of two existing lines of work, namely skeletal motion retargeting, which leverages long-term temporal context, and surface-based retargeting, which preserves surface details, by combining a geometry-aware deformation model with a skeleton-aware motion transfer approach. This allows to take into account long-term temporal context while accounting for surface details. During inference, our method runs online, i.e. input can be processed in a serial way, and retargeting is performed in a single forward pass per frame. Experiments show that including long-term temporal context during training improves the method's accuracy for skeletal motion and detail preservation. Furthermore, our method generalizes to unobserved motions and body shapes. We demonstrate that our method achieves state-of-the-art results on two test datasets and that it can be used to animate human models with the output of a multi-view acquisition platform. Code is available at urlhttps://gitlab.inria.fr/rrekikdi/human-motion-retargeting2023.