# PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis

arXiv: https://arxiv.org/abs/2603.14409 · HTML: https://mlsanity.com/paper/2603.14409/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 15 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [Morph: A Motion-free Physics Optimization Framework for Human Motion Generation](https://mlsanity.com/paper/2411.14951/) (2024)
- [GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition](https://mlsanity.com/paper/2407.14812/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/2606.06867/) (2026)

## Abstract (arXiv)

Pathological gait analysis is constrained by limited and variable clinical datasets, which restrict the modeling of diverse gait impairments. To address this challenge, we propose a Pathological Gait-conditioned Generative Adversarial Network (PGcGAN) that synthesises pathology-specific gait sequences directly from observed 3D pose keypoint trajectories data. The framework incorporates one-hot encoded pathology labels within both the generator and discriminator, enabling controlled synthesis across six gait categories. The generator adopts a conditional autoencoder architecture trained with adversarial and reconstruction objectives to preserve structural and temporal gait characteristics. Experiments on the Pathological Gait Dataset demonstrate strong alignment between real and synthetic sequences through PCA and t-SNE analyses, visual kinematic inspection, and downstream classification tasks. Augmenting real data with synthetic sequences improved pathological gait recognition across GRU, LSTM, and CNN models, indicating that pathology-conditioned gait synthesis can effectively support data augmentation in pathological gait analysis.