# Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions

arXiv: https://arxiv.org/abs/2306.04161 · HTML: https://mlsanity.com/paper/2306.04161/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 7 Jun 2023.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Neural probabilistic motor primitives for humanoid control](https://mlsanity.com/paper/1811.11711/) (2018)
- [QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars](https://mlsanity.com/paper/2209.09391/) (2022)
- [Learning Symmetric and Low-energy Locomotion](https://mlsanity.com/paper/1801.08093/) (2018)
- [Learning to Use Chopsticks in Diverse Gripping Styles](https://mlsanity.com/paper/2205.14313/) (2022)
- [Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation](https://mlsanity.com/paper/1904.13041/) (2019)
- [Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Users](https://mlsanity.com/paper/2209.05753/) (2022)
- [Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments](https://mlsanity.com/paper/1804.00361/) (2018)
- [AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control](https://mlsanity.com/paper/2104.02180/) (2021)
- [Artificial Intelligence for Prosthetics - challenge solutions](https://mlsanity.com/paper/1902.02441/) (2019)
- [Generative GaitNet](https://mlsanity.com/paper/2201.12044/) (2022)
- [CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion](https://mlsanity.com/paper/2005.03288/) (2020)
- [NIMBLE: A Non-rigid Hand Model with Bones and Muscles](https://mlsanity.com/paper/2202.04533/) (2022)
- [Functionality-Driven Musculature Retargeting](https://mlsanity.com/paper/2007.15311/) (2020)

## Built on by (most cited first, 3 of 3)

- [MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning](https://mlsanity.com/paper/2411.16964/) (2024)
- [Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation](https://mlsanity.com/paper/2601.22550/) (2026)
- [From 3D Pose to Prose: Biomechanics-Grounded Vision--Language Coaching](https://mlsanity.com/paper/2603.26938/) (2026)

## Abstract (arXiv)

We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait. The simulation model of human anatomy is a comprehensive, full-body, simulation-ready, musculoskeletal model with 304 Hill-type musculotendon units. The Bidirectional GaitNet consists of forward and backward models. The forward model predicts a gait pattern of a person with specific physical conditions, while the backward model estimates the physical conditions of a person when his/her gait pattern is provided. Our simulation-based approach first learns the forward model by distilling the simulation data generated by a state-of-the-art predictive gait simulator and then constructs a Variational Autoencoder (VAE) with the learned forward model as its decoder. Once it is learned its encoder serves as the backward model. We demonstrate our model on a variety of healthy/impaired gaits and validate it in comparison with physical examination data of real patients.