## 2 Background Works
### Applications of AI in Dance Movement Analysis and Creation  
Research in dance movement analysis and generation through machine learning and artificial intelligence has demonstrated the potential to transform choreography and performance, with various studies exploring these applications. For example, [PERSON] and [PERSON] introduced Chor-RNN, a system that generates choreography reflecting a dancer's nuanced choreographic language and style. Rather than merely generating simple movement sequences, Chor-RNN produces structurally cohesive choreography, showing promising results. The core of this system is a deep recurrent neural network, trained on raw motion capture data to create new dance sequences for solo dancers. This approach extracts high-level features from data, unlocking new possibilities for computer-generated choreography and indicating the potential of machine learning in transforming dance creation and performance [14]. Furthermore, Google Arts & Culture Lab collaborated with renowned choreographer [PERSON] to develop an AI-based choreographic tool utilizing machine learning. This project, known as Living Archive, used U-MAP to map and organize approximately 500,000 moments from [PERSON]'s 25-year choreography archive based on visual similarity. In a related study, [PERSON] et al. proposed a \"Do As I Do\" motion transfer technique [8]. This approach enables the transfer of standard movements from a source dancer to a new (often amateur) target dancer. Using poses as intermediate representations, this method extracts poses from the source and applies a learned pose-to-appearance mapping to the target, effectively translating movements across subjects. Collectively, these studies illustrate how machine learning can influence the creation and performance of dance. The availability of extensive datasets is essential in machine learning, particularly for dance studies. Dance performances provide abundant motion data that enable algorithms to learn from diverse dance video collections. Such data allow algorithms to identify inherent patterns and structures within various dance styles, fostering a deeper understanding of movement diversity. For instance, a dataset titled Martial Arts, Dancing, and Sports Dataset for 3D Human Pose Estimation includes challenging movements from tai chi, karate, jazz, hip-hop, and sports, captured across 30 multi-view and stereo depth videos. This dataset, recorded using multiple cameras and involving dancers and martial artists, is valuable for examining complex movements under diverse scenarios [32]. In the realm of 2D pose recognition, datasets such as Leeds Sports Pose (LSP), containing approximately 25,000 images with 40,000 individuals in sports contexts, have been widely used, with images sourced from YouTube videos. The MPII Human Pose dataset includes 25,000 images with detailed annotations, such as joint coordinates, body part occlusion, 3D torso and head orientation, and 410 activity labels. Larger datasets, such as MS COCO, containing 60,000 images and 150,000 individuals, and AI Challenger, with 300,000 images and 700,000 individuals, offer extensive resources for pose estimation tasks [33,34]. The Human3.6M dataset provides 3.6 million images from a lab setting, captured using markers on 11 subjects performing various scenarios [35]. Similarly, datasets like MPI-INF-3 DHP and CMU Panoptic offer diverse scenarios and sophisticated setups, serving as essential resources for complex human pose research. Studies on Indian classical dance have also utilized convolutional neural networks (CNNs) for movement classification. A CNN architecture, designed with four convolutional layers and different filter window sizes, was proposed to improve recognition speed and accuracy. This architecture employs probabilistic pooling, combining maxand average pooling techniques, and was trained on a dataset of 10 dancers performing 200 mudras/poses across various backgrounds [9; 10; 11].