The lack of fine-grained joints such as hand fingers is a fundamental performance bottleneck for state of the art skeleton action recognition models trained on the largest action recognition dataset, NTU-RGBD. To address this bottleneck, we introduce a new skeleton based human action dataset - NTU60-X. In addition to the 25 body joints for each skeleton as in NTU-RGBD, NTU60-X dataset includes finger and facial joints, enabling a richer skeleton representation. We appropriately modify the state of the art approaches to enable training using the introduced dataset. Our results demonstrate the effectiveness of NTU60-X in overcoming the aforementioned bottleneck and improve state of the art performance, overall and on hitherto worst performing action categories.

中文翻译：

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NTU60-X：迈向基于骨骼的微妙人类动作识别

缺少诸如手指的细小关节是在最大的动作识别数据集NTU-RGBD上训练的最先进骨骼动作识别模型的基本性能瓶颈。为了解决这个瓶颈，我们引入了一个基于骨架的新人类动作数据集-NTU60-X。除了NTU-RGBD中每个骨骼的25个身体关节之外，NTU60-X数据集还包括手指关节和面部关节，可实现更丰富的骨骼表示。我们适当地修改了最新技术方法，以使用引入的数据集进行训练。我们的结果证明了NTU60-X在克服上述瓶颈和改善总体性能以及迄今为止表现最差的动作类别方面的有效性。