With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language 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 experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code are publicly available at https://andytang15.github.io/FLAG3D.
@article{arxiv.2212.04638,
title = {FLAG3D: A 3D Fitness Activity Dataset with Language Instruction},
author = {Yansong Tang and Jinpeng Liu and Aoyang Liu and Bin Yang and Wenxun Dai and Yongming Rao and Jiwen Lu and Jie Zhou and Xiu Li},
journal= {arXiv preprint arXiv:2212.04638},
year = {2023}
}