FLAG3D++: A Benchmark for 3D Fitness Activity Comprehension With Language Instruction

Recent years have witnessed the rapid development of general human action understanding. However, when applied to real-world applications such as sports analysis, most existing datasets are still unsatisfactory, because of the limitations in rich labels on multiple tasks, language instructions, high-quality 3D data, and diverse environments. In this paper, we present FLAG3D++, a large-scale benchmark for 3D fitness activity comprehension, which contains 180 K sequences of 60 activity categories with language instruction. FLAG3D++ features the following four aspects: 1) fine-grained annotations of the temporal intervals of actions in the untrimmed long sequences and how well these actions are performed, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 4) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. In light of the specified features, we present two new practical applications as language-guided repetition action counting (L-RAC) and language-guided action quality assessment (L-AQA), which aim to take the language descriptions as references to count the repetitive times of an action and assess the quality of action respectively. Furthermore, we propose a Hierarchical Language-Guided Graph Convolutional Network (HL-GCN) model to better fuse the language information and skeleton sequences for L-RAC and L-AQA. To be specific, the HL-GCN performs cross-modal alignments by the early fusion of the linguistic feature and the hierarchical node features of the skeleton-based sequences encoded by the multiple intermediate graph convolutional layers. Extensive experiments show the superiority of our HL-GCN on both L-RAC and L-AQA, as well as the great research value of FLAG3D++ for various challenges, such as dynamic human mesh recovery and cross-domain human action recognition. Our dataset, source code, and trained models are made publicly available at FLAG3D++.