用生成模型扩充动作识别数据，小样本下也能提升精度。
Generative Data Augmentation for Skeleton Action Recognition
- 基于Transformer的条件生成架构，约束动作标签生成骨架序列。
- 在少量训练数据下仍能生成高质量序列，准确率显著提升。
- 适合数据稀缺场景，尤其对小样本动作识别研究者有帮助。
基于骨架的人体动作识别能有效从姿态数据中理解人类行为，但收集大规模、多样化且标注精确的3D骨架数据集成本高、耗时长。为解决此问题，我们提出一种用于骨架动作识别的数据增强条件生成流水线。该方法在动作标签约束下学习真实骨架序列的分布，从而合成多样且高保真的数据。即使训练样本有限，也能有效生成骨架序列，并在低数据场景下实现具有竞争力的识别性能，展现出强泛化能力。具体地，我们引入基于Transformer的编码器-解码器架构，结合生成优化模块与丢弃机制，在采样过程中平衡保真度与多样性。在HumanAct12和改进版NTU-RGBD（NTU-VIBE）数据集上的实验表明，该方法持续提升了多个基于骨架的动作识别模型的准确率，验证了其在少样本与全数据设置下的有效性。源代码可在此处获取。
原文摘要 · Abstract (English)
Skeleton-based human action recognition is a powerful approach for understanding human behaviour from pose data, but collecting large-scale, diverse, and well-annotated 3D skeleton datasets is both expensive and labor-intensive. To address this challenge, we propose a conditional generative pipeline for data augmentation in skeleton action recognition. Our method learns the distribution of real skeleton sequences under the constraint of action labels, enabling the synthesis of diverse and high-fidelity data. Even with limited training samples, it can effectively generate skeleton sequences and achieve competitive recognition performance in low-data scenarios, demonstrating strong generalisation in downstream tasks. Specifically, we introduce a Transformer-based encoder-decoder architecture, combined with a generative refinement module and a dropout mechanism, to balance fidelity and diversity during sampling. Experiments on HumanAct12 and the refined NTU-RGBD (NTU-VIBE) dataset show that our approach consistently improves the accuracy of multiple skeleton-based action recognition models, validating its effectiveness in both few-shot and full-data settings. The source code can be found at here.
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