# Human Motion Transfer with 3D Constraints and Detail Enhancement

arXiv: https://arxiv.org/abs/2003.13510 · HTML: https://mlsanity.com/paper/2003.13510/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion](https://mlsanity.com/paper/2311.12052/) (2023)
- [GUAVA: Generalizable Upper Body 3D Gaussian Avatar](https://mlsanity.com/paper/2505.03351/) (2025)
- [SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence](https://mlsanity.com/paper/2304.05395/) (2023)
- [A Shape-Aware Retargeting Approach to Transfer Human Motion and Appearance in Monocular Videos](https://mlsanity.com/paper/2103.15596/) (2021)
- [Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence](https://mlsanity.com/paper/2503.21766/) (2025)
- [Robust Pose Transfer with Dynamic Details using Neural Video Rendering](https://mlsanity.com/paper/2106.14132/) (2021)
- [A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer](https://mlsanity.com/paper/2109.11719/) (2021)
- [Render In-between: Motion Guided Video Synthesis for Action Interpolation](https://mlsanity.com/paper/2111.01029/) (2021)
- [An Identity-Preserved Framework for Human Motion Transfer](https://mlsanity.com/paper/2204.06862/) (2022)
- [Gaussian See, Gaussian Do: Semantic 3D Motion Transfer from Multiview Video](https://mlsanity.com/paper/2511.14848/) (2025)
- [SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals](https://mlsanity.com/paper/2605.18039/) (2026)
- [3D Point Cloud Registration with Learning-based Matching Algorithm](https://mlsanity.com/paper/2202.02149/) (2022)
- [Motion Marionette: Rethinking Rigid Motion Transfer via Prior Guidance](https://mlsanity.com/paper/2511.19909/) (2025)
- [EMOSH: Expressive Motion and Shape Disentanglement for Human Animation](https://mlsanity.com/paper/2606.28026/) (2026)

## Abstract (arXiv)

We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.