# 3D Human Motion Prediction: A Survey

arXiv: https://arxiv.org/abs/2203.01593 · HTML: https://mlsanity.com/paper/2203.01593/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 114 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 30 of 42)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [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)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [DLow: Diversifying Latent Flows for Diverse Human Motion Prediction](https://mlsanity.com/paper/2003.08386/) (2020)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [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)
- [Human Motion Prediction via Spatio-Temporal Inpainting](https://mlsanity.com/paper/1812.05478/) (2018)
- [QuaterNet: A Quaternion-based Recurrent Model for Human Motion](https://mlsanity.com/paper/1805.06485/) (2018)
- [Deep representation learning for human motion prediction and classification](https://mlsanity.com/paper/1702.07486/) (2017)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Structured Prediction Helps 3D Human Motion Modelling](https://mlsanity.com/paper/1910.09070/) (2019)
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://mlsanity.com/paper/1808.05819/) (2018)
- [A Neural Temporal Model for Human Motion Prediction](https://mlsanity.com/paper/1809.03036/) (2018)
- [Context-aware Human Motion Prediction](https://mlsanity.com/paper/1904.03419/) (2019)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Learning Human Motion Models for Long-term Predictions](https://mlsanity.com/paper/1704.02827/) (2017)
- [BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN](https://mlsanity.com/paper/1812.02591/) (2018)
- [Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers](https://mlsanity.com/paper/2109.07531/) (2021)
- [Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic](https://mlsanity.com/paper/1805.02513/) (2018)
- [Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction](https://mlsanity.com/paper/2108.11244/) (2021)
- [Multi-level Motion Attention for Human Motion Prediction](https://mlsanity.com/paper/2106.09300/) (2021)
- [Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies](https://mlsanity.com/paper/1902.07367/) (2019)

## Built on by (most cited first, 30 of 30)

- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction](https://mlsanity.com/paper/2211.14304/) (2022)
- [Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction](https://mlsanity.com/paper/2501.06035/) (2025)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [From Sparse Signal to Smooth Motion: Real-Time Motion Generation with Rolling Prediction Models](https://mlsanity.com/paper/2504.05265/) (2025)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [Text-driven Motion Generation: Overview, Challenges and Directions](https://mlsanity.com/paper/2505.09379/) (2025)
- [Motion Generation: A Survey of Generative Approaches and Benchmarks](https://mlsanity.com/paper/2507.05419/) (2025)
- [3D Human Interaction Generation: A Survey](https://mlsanity.com/paper/2503.13120/) (2025)
- [Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting](https://mlsanity.com/paper/2402.19237/) (2024)
- [Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction](https://mlsanity.com/paper/2405.18700/) (2024)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning](https://mlsanity.com/paper/2605.30969/) (2026)
- [DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network](https://mlsanity.com/paper/2209.09124/) (2022)
- [Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction](https://mlsanity.com/paper/2312.08558/) (2023)
- [Recent Advances in Deterministic Human Motion Prediction: A Review](https://mlsanity.com/paper/2312.06184/) (2023)
- [Fooling Neural Networks for Motion Forecasting via Adversarial Attacks](https://mlsanity.com/paper/2403.04954/) (2024)
- [Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward](https://mlsanity.com/paper/2506.03191/) (2025)
- [GraphEnet: Event-driven Human Pose Estimation with a Graph Neural Network](https://mlsanity.com/paper/2510.07990/) (2025)
- [MMCM: Multimodality-aware Metric using Clustering-based Modes for Probabilistic Human Motion Prediction](https://mlsanity.com/paper/2511.15179/) (2025)
- [Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editing](https://mlsanity.com/paper/2608.16008/) (2026)
- [STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead](https://mlsanity.com/paper/2209.07600/) (2022)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [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)
- [A Real-Time Human Action Recognition Model for Assisted Living](https://mlsanity.com/paper/2503.18957/) (2025)
- [Pattern-Guided Diffusion Models](https://mlsanity.com/paper/2512.13945/) (2025)
- [MoPO: Incorporating Motion Prior for Occluded Human Mesh Recovery](https://mlsanity.com/paper/2605.09856/) (2026)
- [Scene-aware Prediction of Diverse Human Movement Goals](https://mlsanity.com/paper/2606.29942/) (2026)
- [KOALA: Koopman Operator Learning for WiFi-Based Anticipatory Hum](https://mlsanity.com/paper/2608.15815/) (2026)

## Abstract (arXiv)

3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and textitprediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.