The increasing popularity of exercises including yoga and Pilates has created a greater demand for professional exercise video datasets in the realm of artificial intelligence. In this study, we developed 3DYoga901, which is organized within a three-level label hierarchy. We have expanded the number of poses from an existing state-of-the-art dataset, increasing it from 82 to 90 poses. Our dataset includes meticulously curated RGB yoga pose videos and 3D skeleton sequences. This dataset was created by a dedicated team of six individuals, including yoga instructors. It stands out as one of the most comprehensive open datasets, featuring the largest collection of RGB videos and 3D skeleton sequences among publicly available resources. This contribution has the potential to significantly advance the field of yoga action recognition and pose assessment. Additionally, we conducted experiments to evaluate the practicality of our proposed dataset. We employed three different model variants for benchmarking purposes.

翻译：瑜伽和普拉提等运动的日益普及，使得人工智能领域对专业运动视频数据集的需求大幅增长。本研究开发了3DYoga90数据集，其组织结构采用三级标签层级。相较于现有最优数据集，我们将姿态数量从82个扩展至90个。该数据集包含精心整理的RGB瑜伽姿态视频及3D骨骼序列，由包括瑜伽教练在内的六人专业团队创建。作为公开资源中RGB视频与3D骨骼序列规模最大的综合性开放数据集，该成果有望显著推动瑜伽动作识别与姿态评估领域的发展。此外，我们通过实验验证了所提数据集的实用性，并采用三种不同模型变体进行基准测试。