  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.
