具身认知
即兴创作
舞蹈
声音(地理)
运动(物理)
计算机科学
运动捕捉
声学
人工智能
人机交互
视觉艺术
艺术
物理
作者
Benedikte Wallace,Charles Martín,Jim Tørresen,Kristian Nymoen
出处
期刊:Creativity and Cognition
日期:2021-06-22
被引量:5
标识
DOI:10.1145/3450741.3465245
摘要
Through dance, a wide range of emotions can be expressed. As virtual agents and robots continue to become part of our daily lives, the need for them to efficiently convey emotion and intent increases. When trained to dance, to what extent can AI learn to model the tacit mappings between sound and motion? Here, we explore the creative capacity of a generative model trained on 3D motion capture recordings of improvised dance. We perform a perceptual judgment experiment wherein respondents rate movement generated by our model as well as human performances. While the sound-motion mappings remain somewhat elusive, particularly when compared to examples of human dance, our study shows that in certain aspects related to perceived dance-likeness and expressivity, the model successfully mimics human dance movement. By employing a perceptual study to evaluate our generative model, we aim to further our ability to understand the affordances and limitations of creative AI.
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