WiFi-based human pose estimation has garnered significant interest in deep learning research. However, due to the varying angles of signal transceivers and the differing sensitivities of signal subcarriers to movement, inaccuracies can arise in WiFi-based human pose estimation. For instance, when a person is within a WiFi field, local changes in one or more channels and directions of structure can be detected. This channel interaction generally involves mutual interference, modifying movement localization, and perception sensitivity. To achieve unambiguous localization and identification, we decompose the properties of the Channel State Information spatial structure and its behavior, demonstrating that dual-view observation—spatial direction and channel sensitivity—is sufficient. Furthermore, we propose a CSI-Channel Spatial Decomposition Strategy (CSDS). Specifically, we introduce the Spatial Orientation Attention Module (SOA), which employs angle-dependent weighting to mitigate the error induced by signal transceiver pairs with deviated angles relative to the human body. Subsequently, the Spatial Sensitivity Enhancement Module (SSE) addresses errors from low-sensitivity signal carriers for motion detection by employing channel decoupling. Applying these two modules enables the model to discern potentially valid human pose information more effectively in WiFi transmission signals. The experimental results on the Wi-Pose public dataset demonstrate the effectiveness of CSDS.