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.