3DInAction: Understanding Human Actions in 3D Point Clouds

• URL: http://arxiv.org/abs/2303.06346v2
• Date: Fri, 29 Mar 2024 15:10:29 GMT
• Title: 3DInAction: Understanding Human Actions in 3D Point Clouds
• Authors: Yizhak Ben-Shabat, Oren Shrout, Stephen Gould,
• Abstract summary: We propose a novel method for 3D point cloud action recognition. We show that our method achieves improved performance on existing datasets, including ASM videos.
• Score: 31.66883982183386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel method for 3D point cloud action recognition. Understanding human actions in RGB videos has been widely studied in recent years, however, its 3D point cloud counterpart remains under-explored. This is mostly due to the inherent limitation of the point cloud data modality -- lack of structure, permutation invariance, and varying number of points -- which makes it difficult to learn a spatio-temporal representation. To address this limitation, we propose the 3DinAction pipeline that first estimates patches moving in time (t-patches) as a key building block, alongside a hierarchical architecture that learns an informative spatio-temporal representation. We show that our method achieves improved performance on existing datasets, including DFAUST and IKEA ASM. Code is publicly available at https://github.com/sitzikbs/3dincaction.

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