Dance Generation by Sound Symbolic Words
This addresses the problem of making dance generation more accessible and diverse for a wider audience across different languages and cultures, though it appears incremental by adapting existing frameworks.
This study tackled the problem of generating dance motions by using onomatopoeia as input to enhance creativity and diversity, resulting in a method that enables more intuitive dance generation and can create motions using sound-symbolic words from various languages.
This study introduces a novel approach to generate dance motions using onomatopoeia as input, with the aim of enhancing creativity and diversity in dance generation. Unlike text and music, onomatopoeia conveys rhythm and meaning through abstract word expressions without constraints on expression and without need for specialized knowledge. We adapt the AI Choreographer framework and employ the Sakamoto system, a feature extraction method for onomatopoeia focusing on phonemes and syllables. Additionally, we present a new dataset of 40 onomatopoeia-dance motion pairs collected through a user survey. Our results demonstrate that the proposed method enables more intuitive dance generation and can create dance motions using sound-symbolic words from a variety of languages, including those without onomatopoeia. This highlights the potential for diverse dance creation across different languages and cultures, accessible to a wider audience. Qualitative samples from our model can be found at: https://sites.google.com/view/onomatopoeia-dance/home/.