<p>Contact-free sensing based on WiFi channel state information (CSI) has shown considerable potential for human activity recognition and indoor localization. However, jointly addressing these two tasks remains challenging because raw CSI signals usually suffer from high-dimensional channel redundancy, task-irrelevant variations, and temporally entangled multi-scale fluctuations. To address these issues, this paper proposes a dual-task learning framework that emphasizes task-aligned subspace construction and structured temporal decomposition. Specifically, a Multi-task Aligned Re-ranking Subspace Principal Component Analysis (MARS-PCA) module is designed to re-rank principal components according to their discriminative relevance to both activity recognition and localization, thereby retaining a compact CSI representation that is more consistent with the dual-task objective. In addition, a multi-level wavelet decomposition front-end is introduced to separate CSI temporal responses into sub-band components, allowing transient activity-related dynamics and relatively stable location-related patterns to be represented more explicitly. The refined and decomposed features are then modeled by a lightweight temporal prediction module with channel-wise task regulation. Experiments on a public WiFi CSI dataset show that the proposed method achieves good performance in both activity recognition and indoor localization.</p>