Transformer-based partner dance motion generation

With the rapid development of information technology, particularly the recent breakthroughs in the field of artificial intelligence, the field of dance education has experienced an unprecedented transformation. Today, an increasing number of dance enthusiasts can experience an efficient and personalized immersive dance learning process via advanced artificial intelligence applications. Compared with the individual practice of solo dance, the learning process of partner dancing is more complex. Partner dancing requires not only a coordinated dance partner but also the abilities of both individuals to accurately follow each other's movements in real time as well as adjust and present the correct dance postures. In this study, we developed an innovative real-time virtual reality dance training framework tailored for interactive entertainment and teaching. Our framework used an enhanced transformer model that could generate partner movement sequences based on user movements. Furthermore, we established a somatosensory virtual reality interaction environment by integrating somatosensory devices to capture user movements in real time. These movement sequences were then fed into a network to produce partner movement sequences by generating virtual characters and creating an interactive dance learning system that can offer real-time feedback from a virtual partner. Our experiments validated the robustness and effectiveness of the proposed sequence generation model. In addition, we incorporated Laban movement analysis to explore the semantic representations of partner dances and devised a comprehensive set of evaluation indicators to assess the quality of the generated partner dance movements. The rigorous testing of each component and actual user testing demonstrated the effectiveness of the system. The system offered users rich interactive feedback and a scientifically grounded approach for learning to dance with a partner. Therefore, this study offers significant prospects for both intelligent dance teaching and human-computer interaction in virtual reality among other applications.