用文字描述生成病态步态数据，解决真实数据稀缺问题。
LLM-Conditioned Synthesis of Pathological Gaits via Structured Gait-Language Representations
- 通过结构化文本描述与LLM增强，生成带病理特征的3D步态序列。
- 合成数据+真实数据训练使分类准确率达92.77%（留一被试者验证）。
- 适合医学影像生成、罕见病数据扩充等研究方向。
由于隐私、招募难度、成本及运动变异性，病态步态数据集仍十分稀缺。本文提出一种多模态大语言模型引导的框架，基于结构化文本描述生成具有病理感知的3D步态数据。该方法生成固定长度的骨骼基步态序列，用于病态步态分类任务。框架融合动作分词、病理感知语言条件化、LLM语义增强与语言到步态生成机制。核心贡献为设计的病态分词器，可在离散表示学习中保留病理特异性运动特征。实验表明，将合成序列与真实数据结合后，下游分类性能提升；最佳结果为使用真实与合成样本训练的GRU分类器，在留一被试者验证协议下达到92.77%准确率。
原文摘要 · Abstract (English)
Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framework for pathology-aware 3D gait data synthesis from structured textual descriptions. The proposed method generates fixed-length synthetic skeleton-based gait sequences for pathological gait classification tasks. The framework combines motion tokenisation, pathology-aware language conditioning, LLM-based semantic augmentation, and language-to-gait generation. A key contribution is the proposed pathological tokeniser, which is designed to preserve pathology-specific motion characteristics during discrete representation learning. Experiments suggest that the proposed synthetic sequences improve downstream classification for recurrent classifiers when combined with real data. The best result is obtained using a GRU classifier trained with real and synthetic samples, achieving 92.77\% accuracy under a leave-one-subject-out protocol.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。