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With the continuously thriving popularity around the world, fitness activityanalytic has become an emerging research topic in computer vision. While avariety of new tasks and algorithms have been proposed recently, there aregrowing hunger for data resources involved in high-quality data, fine-grainedlabels, and diverse environments. In this paper, we present FLAG3D, alarge-scale 3D fitness activity dataset with language instruction containing180K sequences of 60 categories. FLAG3D features the following three aspects:1) accurate and dense 3D human pose captured from advanced MoCap system tohandle the complex activity and large movement, 2) detailed and professionallanguage instruction to describe how to perform a specific activity, 3)versatile video resources from a high-tech MoCap system, rendering software,and cost-effective smartphones in natural environments. Extensive experimentsand in-depth analysis show that FLAG3D contributes great research value forvarious challenges, such as cross-domain human action recognition, dynamichuman mesh recovery, and language-guided human action generation. Our datasetand source code are publicly available at https://andytang15.github.io/FLAG3D.

FLAG3D: A 3D Fitness Activity Dataset with Language Instruction