In this paper, we study current and upcoming frontiers across the landscape
of skeleton-based human action recognition. To study skeleton-action
recognition in the wild, we introduce Skeletics-152, a curated and 3-D
pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale
action dataset. We extend our study to include out-of-context actions by
introducing Skeleton-Mimetics, a dataset derived from the recently introduced
Mimetics dataset. We also introduce Metaphorics, a dataset with caption-style
annotated YouTube videos of the popular social game Dumb Charades and
interpretative dance performances. We benchmark state-of-the-art models on the
NTU-120 dataset and provide multi-layered assessment of the results. The
results from benchmarking the top performers of NTU-120 on the newly introduced
datasets reveal the challenges and domain gap induced by actions in the wild.
Overall, our work characterizes the strengths and limitations of existing
approaches and datasets. Via the introduced datasets, our work enables new
frontiers for human action recognition.

本文研究骨架为基础的人体动作识别的当前和未来发展方向，以 Skeletics-152、Skeleton-Mimetics、Metaphorics 和 NTU-120 等数据集作为研究对象，旨在评估现有方法和数据集的优劣，并开辟人体动作识别的新领域。