WiFi-based gesture recognition has emerged as a promising RF sensing paradigm for enabling non-contact and privacy-preserving human-computer interaction in AIoT environments. However, existing methods often suffer from limited generalization and semantic expressiveness due to the domain-sensitive nature of Channel State Information and the lack of high-level gesture abstraction. To address these challenges, we propose a novel generalization framework, termed Large-Model-Aware Semantic Distillation and Alignment (GLSDA), which leverages the semantic prior of pre-trained large foundation models to enhance gesture representation learning in both in-domain and cross-domain scenarios. Specifically, we first design a dual-path CSI encoding pipeline that captures geometric and dynamic gesture patterns via CSI-Ratio phase sequences and Doppler spectrograms. These representations are then fed into a Multiscale Semantic Encoder, which learns robust temporal embeddings and aligns them with gesture semantics through cross-modal attention mechanisms. To further enhance category discrimination, we introduce a Semantic-Aware Soft Supervision scheme that encodes inter-class correlations and reduces label ambiguity, especially for semantically similar gestures. Finally, we develop a Robust Dual-Distillation strategy to compress the aligned model into a lightweight student network, jointly distilling intermediate features and semantic-informed soft labels from the teacher model. Extensive experiments on the Widar3.0 benchmark show that GLSDA consistently outperforms state-of-the-art methods in both in-domain and cross-domain gesture recognition tasks, while significantly reducing model size and inference latency. Our method offers a scalable and deployable solution for generalized RF-based gesture interfaces in real-world AIoT applications.

翻译：基于WiFi的手势识别已成为人工智能物联网环境中实现非接触式且保护隐私的人机交互的一种有前景的射频感知范式。然而，由于信道状态信息的领域敏感性以及缺乏高层级手势抽象，现有方法通常存在泛化能力有限和语义表达能力不足的问题。为应对这些挑战，我们提出了一种新颖的泛化框架，称为大模型感知语义蒸馏与对齐，该框架利用预训练大型基础模型的语义先验，以增强领域内和跨领域场景下的手势表征学习。具体而言，我们首先设计了一个双路径信道状态信息编码流水线，通过信道状态信息比率相位序列和多普勒频谱图来捕捉几何和动态手势模式。这些表征随后被输入到一个多尺度语义编码器中，该编码器学习鲁棒的时间嵌入，并通过跨模态注意力机制将其与手势语义对齐。为进一步增强类别区分能力，我们引入了一种语义感知软监督方案，该方案编码类间相关性并减少标签模糊性，特别是对于语义相似的手势。最后，我们开发了一种鲁棒的双蒸馏策略，将对齐后的模型压缩为一个轻量级学生网络，联合从教师模型中蒸馏中间特征和语义信息化的软标签。在Widar3.0基准测试上的大量实验表明，GLSDA在领域内和跨领域手势识别任务中均持续优于最先进的方法，同时显著减少了模型大小和推理延迟。我们的方法为现实世界人工智能物联网应用中的通用射频手势接口提供了一个可扩展且可部署的解决方案。