  The lack of fine-grained joints (facial joints, hand fingers) is a
fundamental performance bottleneck for state of the art skeleton action
recognition models. Despite this bottleneck, community's efforts seem to be
invested only in coming up with novel architectures. To specifically address
this bottleneck, we introduce two new pose based human action datasets -
NTU60-X and NTU120-X. Our datasets extend the largest existing action
recognition dataset, NTU-RGBD. In addition to the 25 body joints for each
skeleton as in NTU-RGBD, NTU60-X and NTU120-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
datasets. Our results demonstrate the effectiveness of these NTU-X datasets in
overcoming the aforementioned bottleneck and improve state of the art
performance, overall and on previously worst performing action categories. Code
and pretrained models can be found at https://github.com/skelemoa/ntu-x .
