PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis

• URL: http://arxiv.org/abs/2603.14409v1
• Date: Sun, 15 Mar 2026 14:50:23 GMT
• Title: PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis
• Abstract summary: We propose a Pathological Gait-conditioned Generative Adversarial Network (PGcGAN) that synthesises pathology-specific gait sequences directly from observed 3D pose keypoint trajectories data.<n>The framework incorporates one-hot encoded pathology labels within both the generator and discriminator, enabling controlled synthesis across six gait categories.
• Score: 11.395610680393276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology [54.42160154368719]
  Whole-slide images (WSI) have enabled the digital transformation of pathology.<n> multimodal models that jointly analyze histopathology images and pathology reports have shown promising potential for automated pathology report generation and AI-assisted diagnosis.<n>Here, we introduce TUM-Uteria, a uterine pathology dataset comprising WSIs paired with diagnostic pathology reports at both the case and slide levels.
  arXiv  Detail & Related papers  (2026-07-04T20:34:59Z)
• LLM-Conditioned Synthesis of Pathological Gaits via Structured Gait-Language Representations [11.395610680393276]
  Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability.<n>Our work presents a multimodal LLM-guided framework for pathology-aware 3D gait data synthesis from structured textual descriptions.<n>The framework combines motion tokenisation, pathology-aware language conditioning, LLM-based semantic augmentation, and language-to-gait generation.
  arXiv  Detail & Related papers  (2026-06-04T11:43:14Z)
• PathAR: Structure-First Autoregressive Synthesis of Multimodal Pathology Images [51.428093790826814]
  We propose pathology Autorgressive modeling (PathAR) to factorize structure and appearance for modality-label-conditioned pathology generation.<n>PathAR stabilizes morphology under heterogeneous modality-specific appearances and enables spatially aligned image-mask pair generation.
  arXiv  Detail & Related papers  (2026-06-01T01:43:48Z)
• Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generation [3.3887144970801555]
  We propose a Graph-Augmented Dual-Stream Medical Report Generation with Topological Internalization, GDMRG.<n>Our framework introduces a Topological Knowledge Internalization module, TKI, which generates an explicit parameterized weight matrix based on global disease co-occurrence priors.<n>Experiments on the MIMIC-CXR dataset demonstrate that GDMRG achieves competitive clinical efficacy, CE, while maintaining natural language fluency.
  arXiv  Detail & Related papers  (2026-05-04T09:17:27Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain [6.001148593361787]
  GAITGen is a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels.<n>Experiments on our new PD-GaM dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality.
  arXiv  Detail & Related papers  (2025-03-28T13:06:45Z)
• Synthetic Data for Robust Stroke Segmentation [0.0]
  Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data.<n>This paper introduces a novel synthetic data framework tailored for stroke lesion segmentation.<n>Our approach trains models with label maps from healthy and stroke datasets, facilitating segmentation across both normal and pathological tissue.
  arXiv  Detail & Related papers  (2024-04-02T13:42:29Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.