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.

在本文中，我们研究了基于骨架的人类动作识别领域中当前和即将出现的前沿。为了研究野外的骨骼动作识别，我们引入了Skeletics-152，这是从大规模动作数据集Kinetics-700中获得的RGB视频的经过策展和3D姿势标注的子集。我们通过引入Skeleton-Mimetics（骨架模型）扩展了我们的研究，以包括上下文外动作，Skeleton-Mimetics是从最近引入的Mimetics数据集获得的数据集。我们还将介绍Metaphorics，这是一个数据集，其中包含流行社交游戏Dumb Charades的字幕样式带注释的YouTube视频以及解释性的舞蹈表演。我们在NTU-120数据集上对最先进的模型进行基准测试，并对结果进行多层评估。在新引入的数据集上对NTU-120的最佳性能进行基准测试的结果揭示了野外行动引起的挑战和领域差距。总体而言，我们的工作体现了现有方法和数据集的优势和局限性。通过引入的数据集，我们的工作为人类动作识别开辟了新的领域。