# Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

arXiv: https://arxiv.org/abs/2507.22742 · HTML: https://mlsanity.com/paper/2507.22742/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 72 references that are in the map.
- First posted on arXiv 30 Jul 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion](https://mlsanity.com/paper/2203.13777/) (2022)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction](https://mlsanity.com/paper/2104.00563/) (2021)
- [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction](https://mlsanity.com/paper/1903.02793/) (2019)
- [EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning](https://mlsanity.com/paper/2303.10876/) (2023)
- [Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World](https://mlsanity.com/paper/1803.08319/) (2018)
- [CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild](https://mlsanity.com/paper/2011.14679/) (2020)
- [MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2108.09274/) (2021)
- [Future Person Localization in First-Person Videos](https://mlsanity.com/paper/1711.11217/) (2017)
- [Collaborative Motion Prediction via Neural Motion Message Passing](https://mlsanity.com/paper/2003.06594/) (2020)
- [Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2206.05712/) (2022)
- [Social-Transmotion: Promptable Human Trajectory Prediction](https://mlsanity.com/paper/2312.16168/) (2023)
- [Are socially-aware trajectory prediction models really socially-aware?](https://mlsanity.com/paper/2108.10879/) (2021)
- [DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting](https://mlsanity.com/paper/2005.12661/) (2020)
- [JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking](https://mlsanity.com/paper/2210.11940/) (2022)
- [Multi-Transmotion: Pre-trained Model for Human Motion Prediction](https://mlsanity.com/paper/2411.02673/) (2024)
- [MSN: Multi-Style Network for Trajectory Prediction](https://mlsanity.com/paper/2107.00932/) (2021)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [Injecting Knowledge in Data-driven Vehicle Trajectory Predictors](https://mlsanity.com/paper/2103.04854/) (2021)

## Built on by (most cited first, 5 of 5)

- [Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models](https://mlsanity.com/paper/2605.15424/) (2026)
- [Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning](https://mlsanity.com/paper/2602.22791/) (2026)
- [Deformable Gaussian Occupancy: Decoupling Rigid and Nonrigid Motion with Factorized Distillation](https://mlsanity.com/paper/2605.28587/) (2026)
- [Three-Step Hierarchical Transformer for Multi-Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2606.23058/) (2026)
- [HumAIN: Human-Aware Implicit Social Robot Navigation](https://mlsanity.com/paper/2607.07357/) (2026)

## Abstract (arXiv)

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this work, we study the benefits of predicting human trajectories using human body poses instead of solely their Cartesian space locations in time. We propose `Social-pose', an attention-based pose encoder that effectively captures the poses of all humans in a scene and their social relations. Our method can be integrated into various trajectory prediction architectures. We have conducted extensive experiments on state-of-the-art models (based on LSTM, GAN, MLP, and Transformer), and showed improvements over all of them on synthetic (Joint Track Auto) and real (Human3.6M, Pedestrians and Cyclists in Road Traffic, and JRDB) datasets. We also explored the advantages of using 2D versus 3D poses, as well as the effect of noisy poses and the application of our pose-based predictor in robot navigation scenarios.