Transflower: probabilistic autoregressive dance generation with multimodal attention

• URL: http://arxiv.org/abs/2106.13871v1
• Date: Fri, 25 Jun 2021 20:14:28 GMT
• Title: Transflower: probabilistic autoregressive dance generation with multimodal attention
• Authors: Guillermo Valle-P\'erez, Gustav Eje Henter, Jonas Beskow, Andr\'e Holzapfel, Pierre-Yves Oudeyer, Simon Alexanderson
• Abstract summary: We present a novel probabilistic autoregressive architecture that models the distribution over future poses with a normalizing flow conditioned on previous poses as well as music context. Second, we introduce the currently largest 3D dance-motion dataset, obtained with a variety of motion-capture technologies, and including both professional and casual dancers.
• Score: 31.308435764603658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a high-dimensional continuous motion signal, conditioned on an audio signal. In this work we make two contributions to tackle this problem. First, we present a novel probabilistic autoregressive architecture that models the distribution over future poses with a normalizing flow conditioned on previous poses as well as music context, using a multimodal transformer encoder. Second, we introduce the currently largest 3D dance-motion dataset, obtained with a variety of motion-capture technologies, and including both professional and casual dancers. Using this dataset, we compare our new model against two baselines, via objective metrics and a user study, and show that both the ability to model a probability distribution, as well as being able to attend over a large motion and music context are necessary to produce interesting, diverse, and realistic dance that matches the music.

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