# Motion Prediction via Joint Dependency Modeling in Phase Space

arXiv: https://arxiv.org/abs/2201.02365 · HTML: https://mlsanity.com/paper/2201.02365/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [DLow: Diversifying Latent Flows for Diverse Human Motion Prediction](https://mlsanity.com/paper/2003.08386/) (2020)
- [History Repeats Itself: Human Motion Prediction via Motion Attention](https://mlsanity.com/paper/2007.11755/) (2020)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Convolutional Sequence to Sequence Model for Human Dynamics](https://mlsanity.com/paper/1805.00655/) (2018)
- [Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure](https://mlsanity.com/paper/1902.08226/) (2019)
- [Learning to Self-Train for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1906.00562/) (2019)
- [BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN](https://mlsanity.com/paper/1812.02591/) (2018)
- [Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies](https://mlsanity.com/paper/1902.07367/) (2019)
- [Aggregated Multi-GANs for Controlled 3D Human Motion Prediction](https://mlsanity.com/paper/2103.09755/) (2021)
- [Unsupervised Online Video Object Segmentation with Motion Property Understanding](https://mlsanity.com/paper/1810.03783/) (2018)
- [Dynamic Future Net: Diversified Human Motion Generation](https://mlsanity.com/paper/2009.05109/) (2020)
- [TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction](https://mlsanity.com/paper/1910.06583/) (2019)
- [SDMTL: Semi-Decoupled Multi-grained Trajectory Learning for 3D human motion prediction](https://mlsanity.com/paper/2010.05133/) (2020)

## Built on by (most cited first, 16 of 16)

- [Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space](https://mlsanity.com/paper/2207.07351/) (2022)
- [3D Human Motion Prediction: A Survey](https://mlsanity.com/paper/2203.01593/) (2022)
- [Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting](https://mlsanity.com/paper/2303.05095/) (2023)
- [Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction](https://mlsanity.com/paper/2204.01297/) (2022)
- [Joint-Motion Mutual Learning for Pose Estimation in Videos](https://mlsanity.com/paper/2408.02285/) (2024)
- [SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction](https://mlsanity.com/paper/2208.09224/) (2022)
- [Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting](https://mlsanity.com/paper/2402.19237/) (2024)
- [Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration](https://mlsanity.com/paper/2403.14104/) (2024)
- [SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos](https://mlsanity.com/paper/2501.15073/) (2025)
- [HVIS: A Human-like Vision and Inference System for Human Motion Prediction](https://mlsanity.com/paper/2502.16913/) (2025)
- [Towards Practical Human Motion Prediction with LiDAR Point Clouds](https://mlsanity.com/paper/2408.08202/) (2024)
- [MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation](https://mlsanity.com/paper/2502.16955/) (2025)
- [HUMOF: Human Motion Forecasting in Interactive Social Scenes](https://mlsanity.com/paper/2506.03753/) (2025)
- [Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward](https://mlsanity.com/paper/2506.03191/) (2025)
- [Personalized Pose Forecasting](https://mlsanity.com/paper/2312.03528/) (2023)
- [Multi-Grained Feature Pruning for Video-Based Human Pose Estimation](https://mlsanity.com/paper/2503.05365/) (2025)

## Abstract (arXiv)

Motion prediction is a classic problem in computer vision, which aims at forecasting future motion given the observed pose sequence. Various deep learning models have been proposed, achieving state-of-the-art performance on motion prediction. However, existing methods typically focus on modeling temporal dynamics in the pose space. Unfortunately, the complicated and high dimensionality nature of human motion brings inherent challenges for dynamic context capturing. Therefore, we move away from the conventional pose based representation and present a novel approach employing a phase space trajectory representation of individual joints. Moreover, current methods tend to only consider the dependencies between physically connected joints. In this paper, we introduce a novel convolutional neural model to effectively leverage explicit prior knowledge of motion anatomy, and simultaneously capture both spatial and temporal information of joint trajectory dynamics. We then propose a global optimization module that learns the implicit relationships between individual joint features. Empirically, our method is evaluated on large-scale 3D human motion benchmark datasets (i.e., Human3.6M, CMU MoCap). These results demonstrate that our method sets the new state-of-the-art on the benchmark datasets. Our code will be available at https://github.com/Pose-Group/TEID.