Wi-Fi sensing systems are severely hindered by domain shifts when deployed in unseen real-world environments. While existing methods attempt to tackle this through Unsupervised Domain Adaptation (UDA) or Domain Generalization (DG), they critically rely on either inaccessible target data or prohibitively expensive, massive labeled source datasets. In practice, collecting abundant unlabeled Channel State Information (CSI) is feasible, whereas manual labeling is severely constrained. This realistic dilemma necessitates Semi-Supervised Domain Generalization (SSDG). To this end, we propose ARC-Fi, the first dedicated SSDG framework for Wi-Fi sensing. Directly applying conventional contrastive learning to CSI data inevitably triggers paradigm-specific "shortcut learning," causing models to memorize environmental backgrounds rather than gesture dynamics. To overcome this, ARC-Fi introduces a physics-informed data augmentation strategy: the Antenna Response Consistency (ARC) module. ARC exploits the intrinsic spatial diversity of multi-antenna systems, treating signals from co-located antennas as naturally semantics-preserving augmented views to explicitly block environmental shortcuts. Furthermore, we introduce a unified Semi-Supervised Contrastive Objective that leverages scarce labels and reliable pseudo-labels to align cross-domain features, effectively preventing the blind repulsion of same-class instances. Extensive experiments on the Widar and CSIDA datasets demonstrate that ARC-Fi establishes a new state-of-the-art, significantly outperforming existing UDA, DG, and SSDG methods. Ultimately, this work provides a physics-grounded, label-efficient solution, advancing the scalable deployment of robust real-world Wi-Fi sensing systems. Code is available at: https://github.com/KaoruMiyazono/UniCrossFi.

翻译：Wi-Fi感知系统在部署于未见过的真实环境时，会因域偏移而严重受限。尽管现有方法尝试通过无监督域适应或域泛化来解决这一问题，但它们严重依赖于不可获取的目标数据或代价高昂的大规模有标签源数据集。实践中，收集大量无标签的信道状态信息是可行的，而人工标注则严重受限。这一现实困境催生了半监督域泛化的需求。为此，我们提出了ARC-Fi，这是首个专用于Wi-Fi感知的SSDG框架。将常规对比学习直接应用于CSI数据会不可避免地触发范式特定的"捷径学习"，导致模型记忆环境背景而非手势动态。为克服此问题，ARC-Fi引入了一种基于物理信息的数据增强策略：天线响应一致性模块。ARC利用多天线系统固有的空间多样性，将共置天线的信号视为天然的语义保持增强视图，以明确阻断环境捷径。此外，我们引入了一个统一的半监督对比目标函数，利用稀少的标签和可靠的伪标签对齐跨域特征，有效防止同类实例的盲目排斥。在Widar和CSIDA数据集上的大量实验表明，ARC-Fi达到了新的最优性能，显著优于现有的UDA、DG和SSDG方法。最终，本工作提供了一种基于物理原理、标签高效的解决方案，推动了鲁棒真实世界Wi-Fi感知系统的可扩展部署。代码可在https://github.com/KaoruMiyazono/UniCrossFi获取。