Fig. 5: Performances using fractions of training data on the PKU-MMD dataset.

We observe that the performance of our approach remains robust even when utilizing only half of the training data. Impressively, with just 10% of the training data, our approach maintains a performance level of 91.9% in the relatively straightforward cross-view evaluation. In contrast, the performance of Beyond-Joints deteriorates when working with reduced fractions of training data. This experiment highlights the scalability of our proposed approach, demonstrating its efficacy even when only a limited amount of labeled data is accessible. It also underscores the low intra-class variance among the action categories within the PKU-MMD dataset. Thus, the creation of a new and challenging 3D action localization benchmark becomes imperative to drive future research in this domain. a Cross-subject evaluation. b Cross-view evaluation. PKU-MMD: Peking University Multi-Modal Dataset.