# Human Motion Prediction Using Manifold-Aware Wasserstein GAN

arXiv: https://arxiv.org/abs/2105.08715 · HTML: https://mlsanity.com/paper/2105.08715/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [Convolutional Sequence to Sequence Model for Human Dynamics](https://mlsanity.com/paper/1805.00655/) (2018)
- [Deep representation learning for human motion prediction and classification](https://mlsanity.com/paper/1702.07486/) (2017)
- [Learning Human Motion Models for Long-term Predictions](https://mlsanity.com/paper/1704.02827/) (2017)
- [A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding](https://mlsanity.com/paper/1807.00676/) (2018)
- [Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets](https://mlsanity.com/paper/1907.10087/) (2019)

## Built on by (most cited first, 8 of 8)

- [Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation](https://mlsanity.com/paper/2401.08559/) (2024)
- [GENMO: A GENeralist Model for Human MOtion](https://mlsanity.com/paper/2505.01425/) (2025)
- [Generation of Complex 3D Human Motion by Temporal and Spatial Composition of Diffusion Models](https://mlsanity.com/paper/2409.11920/) (2024)
- [3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN](https://mlsanity.com/paper/2203.00736/) (2022)
- [Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold](https://mlsanity.com/paper/2412.10458/) (2024)
- [No MoCap Needed: Post-Training Motion Diffusion Models with Reinforcement Learning using Only Textual Prompts](https://mlsanity.com/paper/2510.06988/) (2025)
- [Personalized Pose Forecasting](https://mlsanity.com/paper/2312.03528/) (2023)
- [AdvMT: Adversarial Motion Transformer for Long-term Human Motion Prediction](https://mlsanity.com/paper/2401.05018/) (2024)

## Abstract (arXiv)

Human motion prediction aims to forecast future human poses given a prior pose sequence. The discontinuity of the predicted motion and the performance deterioration in long-term horizons are still the main challenges encountered in current literature. In this work, we tackle these issues by using a compact manifold-valued representation of human motion. Specifically, we model the temporal evolution of the 3D human poses as trajectory, what allows us to map human motions to single points on a sphere manifold. To learn these non-Euclidean representations, we build a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of human motion through different losses. Extensive experiments show that our approach outperforms the state-of-the-art on CMU MoCap and Human 3.6M datasets. Our qualitative results show the smoothness of the predicted motions.