Transflower: probabilistic autoregressive dance generation with multimodal attention

@article{Prez2021TransflowerPA,
  title={Transflower: probabilistic autoregressive dance generation with multimodal attention},
  author={Guillermo Valle P{\'e}rez and Gustav Eje Henter and Jonas Beskow and Andr{\'e} Holzapfel and Pierre-Yves Oudeyer and Simon Alexanderson},
  journal={ACM Trans. Graph.},
  year={2021},
  volume={40},
  pages={195:1-195:14}
}
composition of movements 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… 

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