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Robust Compact Human Pose Learning Against Open-World Visual Perturbations | IEEE Journals & Magazine | IEEE Xplore

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...Show More

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%.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 21, Issue: 6, June 2025)
Page(s): 4789 - 4797
Date of Publication: 14 March 2025

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