Music to Dance as Language Translation using Sequence Models

• URL: http://arxiv.org/abs/2403.15569v1
• Date: Fri, 22 Mar 2024 18:47:54 GMT
• Title: Music to Dance as Language Translation using Sequence Models
• Authors: André Correia, Luís A. Alexandre,
• Abstract summary: We introduce MDLT, a novel approach that frames the choreography generation problem as a translation task. We present two variants of MDLT: one utilising the Transformer architecture and the other employing the Mamba architecture. We train our method on AIST++ and PhantomDance data sets to teach a robotic arm to dance, but our method can be applied to a full humanoid robot.
• Score: 1.4255659581428335
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Synthesising appropriate choreographies from music remains an open problem. We introduce MDLT, a novel approach that frames the choreography generation problem as a translation task. Our method leverages an existing data set to learn to translate sequences of audio into corresponding dance poses. We present two variants of MDLT: one utilising the Transformer architecture and the other employing the Mamba architecture. We train our method on AIST++ and PhantomDance data sets to teach a robotic arm to dance, but our method can be applied to a full humanoid robot. Evaluation metrics, including Average Joint Error and Frechet Inception Distance, consistently demonstrate that, when given a piece of music, MDLT excels at producing realistic and high-quality choreography. The code can be found at github.com/meowatthemoon/MDLT.

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