In this paper, a new perspective is presented for skeleton-based action recognition. Specifically, we regard the skeletal sequence as a spatial-temporal point cloud and voxelize it into a 4-dimensional grid. A novel sparse 4D convolutional network (SC4D) is proposed to directly process the generated 4D grid for high-level perceptions. Without manually designing the hand-crafted transformation rules, it makes better use of the advantages of the convolutional network, resulting in a more concise, general and robust framework for skeletal data. Besides, by processing the space and time simultaneously, it largely keeps the spatial-temporal consistency of the skeletal data, and thus brings better expressiveness. Moreover, with the help of the sparse tensor, it can be efficiently executed with less computations. To verify the superiority of SC4D, extensive experiments are conducted on two challenging datasets, namely, NTU-RGBD and SHREC, where SC4D achieves state-of-the-art performance on both of them.

中文翻译：

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SC4D：用于基于骨架的动作识别的稀疏 4D 卷积网络

在本文中，提出了基于骨架的动作识别的新视角。具体来说，我们将骨骼序列视为时空点云，并将其体素化为 4 维网格。提出了一种新颖的稀疏 4D 卷积网络 (SC4D) 来直接处理生成的 4D 网格以获得高级感知。无需手动设计手工制作的转换规则，它更好地利用了卷积网络的优势，从而为骨骼数据提供了一个更简洁、通用和健壮的框架。此外，通过同时处理空间和时间，在很大程度上保持了骨骼数据的时空一致性，从而带来更好的表现力。此外，在稀疏张量的帮助下，它可以以更少的计算有效地执行。为了验证 SC4D 的优越性，