# Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction

arXiv: https://arxiv.org/abs/1809.03705 · HTML: https://mlsanity.com/paper/1809.03705/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 13 of 13)

- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations](https://mlsanity.com/paper/2309.04840/) (2023)
- [Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks](https://mlsanity.com/paper/2109.05776/) (2021)
- [Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape](https://mlsanity.com/paper/2310.20240/) (2023)
- [Dance Generation by Sound Symbolic Words](https://mlsanity.com/paper/2306.03646/) (2023)
- [DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.09502/) (2025)
- [GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation](https://mlsanity.com/paper/2502.18309/) (2025)
- [HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario](https://mlsanity.com/paper/2509.10096/) (2025)
- [(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2210.04006/) (2022)
- [PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture](https://mlsanity.com/paper/2409.14101/) (2024)
- [Lameness detection in dairy cows using pose estimation and bidirectional LSTMs](https://mlsanity.com/paper/2508.10643/) (2025)
- [TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation](https://mlsanity.com/paper/2604.17005/) (2026)
- [Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2608.15929/) (2026)

## Abstract (arXiv)

In applications such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This paper proposes a biomechanically inspired recurrent neural network (Bio-LSTM) that can predict the location and 3D articulated body pose of pedestrians in a global coordinate frame, given 3D poses and locations estimated in prior frames with inaccuracy. The proposed network is able to predict poses and global locations for multiple pedestrians simultaneously, for pedestrians up to 45 meters from the cameras (urban intersection scale). The outputs of the proposed network are full-body 3D meshes represented in Skinned Multi-Person Linear (SMPL) model parameters. The proposed approach relies on a novel objective function that incorporates the periodicity of human walking (gait), the mirror symmetry of the human body, and the change of ground reaction forces in a human gait cycle. This paper presents prediction results on the PedX dataset, a large-scale, in-the-wild data set collected at real urban intersections with heavy pedestrian traffic. Results show that the proposed network can successfully learn the characteristics of pedestrian gait and produce accurate and consistent 3D pose predictions.