This paper shows skeleton datasets leak sensitive attributes (≈87% gender, ≈80% re-ID) and proposes an adversarial anonymization framework that suppresses private cues while largely preserving action recognition accuracy.

Treating denoising as distribution disentanglement, the paper introduces FDN: an invertible normalizing-flow network that learns noisy-image distributions and masks noise latents to reconstruct clean images, achieving state-of-the-art AWGN/SIDD results efficiently.

The paper introduces two plug-and-play temporal modules: Discrete Cosine Encoding (injecting frequency-aware, noise-robust features) and Chronological Loss (enforcing frame order) that consistently improve diverse skeleton-based action recognizers.

The paper introduces Angular Encoding (AGE): higher-order angular features in static and velocity domains—that fuse seamlessly with spatio-temporal GCN backbones to better disambiguate similar motions, yielding state-of-the-art accuracy with fewer parameters and lower inference cost.