Geo-CF2Net: Geometry-Prior Cross-Frequency Interactive Fusion Network for 3D Human Action Recognition

Dynamic point cloud-based human action recognition has garnered increasing attention due to its inherent advantages in privacy preservation and structural completeness. Current methods typically rely on nested point spatio-temporal convolutions to understand motion semantics in a bottom-up manner, which is intractable for capturing high-fidelity human dynamics disentangled from spatio-temporal interference. Motivated by this, designing a practical spatio-temporal factorization backbone is essential. However, the repeated coarsening of aggregated features along the spatial dimension often leads to the degradation of intrinsic geometric texture relations within point cloud data. Moreover, discretizing continuous visual data into isolated temporal hyperpoints significantly diminishes temporal continuity, resulting in the fragmentation of human action. To circumvent above limitations, we propose a novel Geometry-Prior Cross-Frequency Interactive Fusion Network (Geo-CF2Net). Specifically, we investigate a Spatial-Geometry Pose Prior (SGPP) module, which compensates for pose information loss during spatial downsampling by explicitly modeling geometric constraints among neighboring points. In addition, we elaborate on a Temporal Motion Unit Interactive Coordination (TMIC) module to track the interactive composite semantics of low-frequency steady-state venations and high-frequency transient-state details within a high-dimensional pose evolution flow. Extensive experiments on three public benchmarks substantiate the superiority of Geo-CF2Net over state-of-the-art methods.