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arXiv · 2303.06346

3DInAction: Understanding Human Actions in 3D Point Clouds

Yizhak Ben-Shabat·Oren Shrout·Stephen Gould
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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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Yizhak Ben-Shabat, Oren Shrout, Stephen Gould. 2023-03-11. 3DInAction: Understanding Human Actions in 3D Point Clouds. https://arxiv.org/abs/2303.06346

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