FLAG3D: A 3D Fitness Activity Dataset with Language Instruction

• URL: http://arxiv.org/abs/2212.04638v2
• Date: Wed, 19 Apr 2023 13:31:03 GMT
• Title: FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
• Authors: Yansong Tang, Jinpeng Liu, Aoyang Liu, Bin Yang, Wenxun Dai, Yongming Rao, Jiwen Lu, Jie Zhou, Xiu Li
• Abstract summary: We present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. We 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.
• Score: 89.60371681477791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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