# PVRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction

arXiv: https://arxiv.org/abs/1906.06514 · HTML: https://mlsanity.com/paper/1906.06514/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 20 of 20)

- [3D Human Motion Prediction: A Survey](https://mlsanity.com/paper/2203.01593/) (2022)
- [ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors](https://mlsanity.com/paper/2308.12969/) (2023)
- [Text-controlled Motion Mamba: Text-Instructed Temporal Grounding of Human Motion](https://mlsanity.com/paper/2404.11375/) (2024)
- [Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction](https://mlsanity.com/paper/2204.01297/) (2022)
- [EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds](https://mlsanity.com/paper/2502.05857/) (2025)
- [Learning to Discriminate Information for Online Action Detection: Analysis and Application](https://mlsanity.com/paper/2109.03393/) (2021)
- [Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting](https://mlsanity.com/paper/2402.19237/) (2024)
- [Multimodal Sense-Informed Prediction of 3D Human Motions](https://mlsanity.com/paper/2405.02911/) (2024)
- [Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction](https://mlsanity.com/paper/2405.18700/) (2024)
- [DMS-GCN: Dynamic Mutiscale Spatiotemporal Graph Convolutional Networks for Human Motion Prediction](https://mlsanity.com/paper/2112.10365/) (2021)
- [Efficient Multi-Person Motion Prediction by Lightweight Spatial and Temporal Interactions](https://mlsanity.com/paper/2507.09446/) (2025)
- [PAMD: Plausibility-Aware Motion Diffusion Model for Long Dance Generation](https://mlsanity.com/paper/2505.20056/) (2025)
- [Diverse Motion In-betweening with Dual Posture Stitching](https://mlsanity.com/paper/2303.14457/) (2023)
- [Meta-Auxiliary Learning for Adaptive Human Pose Prediction](https://mlsanity.com/paper/2304.06411/) (2023)
- [Recent Advances in Deterministic Human Motion Prediction: A Review](https://mlsanity.com/paper/2312.06184/) (2023)
- [Relation Learning and Aggregate-attention for Multi-person Motion Prediction](https://mlsanity.com/paper/2411.03729/) (2024)
- [UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Prediction](https://mlsanity.com/paper/2411.04151/) (2024)
- [Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration](https://mlsanity.com/paper/2412.11632/) (2024)
- [GGMotion: Group Graph Dynamics-Kinematics Networks for Human Motion Prediction](https://mlsanity.com/paper/2507.07515/) (2025)
- [Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery](https://mlsanity.com/paper/2606.27900/) (2026)

## Abstract (arXiv)

Human motion prediction, which aims to predict future human poses given past poses, has recently seen increased interest. Many recent approaches are based on Recurrent Neural Networks (RNN) which model human poses with exponential maps. These approaches neglect the pose velocity as well as temporal relation of different poses, and tend to converge to the mean pose or fail to generate natural-looking poses. We therefore propose a novel Position-Velocity Recurrent Encoder-Decoder (PVRED) for human motion prediction, which makes full use of pose velocities and temporal positional information. A temporal position embedding method is presented and a Position-Velocity RNN (PVRNN) is proposed. We also emphasize the benefits of quaternion parameterization of poses and design a novel trainable Quaternion Transformation (QT) layer, which is combined with a robust loss function during training. We provide quantitative results for both short-term prediction in the future 0.5 seconds and long-term prediction in the future 0.5 to 1 seconds. Experiments on several benchmarks show that our approach considerably outperforms the state-of-the-art methods. In addition, qualitative visualizations in the future 4 seconds show that our approach could predict future human-like and meaningful poses in very long time horizons. Code is publicly available on GitHub: textcolorredhttps://github.com/hongsong-wang/PVRNN.