Robust Compact Human Pose Learning Against Open-World Visual Perturbations

Abstract

Skeleton-based action recognition has achieved remarkable progress. However, in open-world scenarios, limited human visual labels, drifting skeletal structures, and novel action categories introduce complex visual disturbances that severely limit the robustness of pose representations. Herein, we propose UnicornPose, a Universal Compact Human Pose Representation, that learns robust skeleton correlations and recognizes action across various open-world scenarios. The core advantages include: 1) Continuously modeling human skeletal structures along the action timeline to construct a rich feature volume of human poses, ensuring sufficient information for universal representation. 2) Utilizing a multiview decoupling method to compress visual information further, obtaining robust pose representations that facilitate easier generalization across different open-world scenarios. 3) Coherence training and regularization constraint methods should be employed to enhance the generalization capability of noise-containing pose representations. These contributions enable UnicornPose to effectively counter noise interference and surpass the existing top results by 3-4%.