Pose2Gest: A Few-Shot Model-Free Approach Applied In South Indian Classical Dance Gesture Recognition
<|reference_start|>Pose2Gest: A Few-Shot Model-Free Approach Applied In South Indian Classical Dance Gesture Recognition: The classical dances from India utilize a set of hand gestures known as Mudras, serving as the foundational elements of its posture vocabulary. Identifying these mudras represents a primary task in digitizing the dance performances. With Kathakali, a dance-drama, as the focus, this work addresses mudra recognition by framing it as a 24-class classification problem and proposes a novel vector-similarity-based approach leveraging pose estimation techniques. This method obviates the need for extensive training or fine-tuning, thus mitigating the issue of limited data availability common in similar AI applications. Achieving an accuracy rate of 92%, our approach demonstrates comparable or superior performance to existing model-training-based methodologies in this domain. Notably, it remains effective even with small datasets comprising just 1 or 5 samples, albeit with a slightly diminished performance. Furthermore, our system supports processing images, videos, and real-time streams, accommodating both hand-cropped and full-body images. As part of this research, we have curated and released a publicly accessible Hasta Mudra dataset, which applies to multiple South Indian art forms including Kathakali. The implementation of the proposed method is also made available as a web application.<|reference_end|>
@article{raju2024pose2gest:,
    title={Pose2Gest: A Few-Shot Model-Free Approach Applied In South Indian
  Classical Dance Gesture Recognition},
    author={Kavitha Raju, Nandini J. Warrier, Manu Madhavan, Selvi C., Arun B.
  Warrier and Thulasi Kumar},
    journal={arXiv preprint arXiv:2404.11205},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11205},
    primaryClass={cs.CV cs.CL}
}