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Intentional Choreography with Semi-Supervised Recurrent VAEs

arXiv 2209.10010 ↗First posted 20 Sep 2022cs.LGcited by 0

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Abstract (arXiv)

We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a small amount of dance sequences labeled with qualitative choreographic annotations, PirouNet conditionally generates dance sequences in the style of the choreographer.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 20 Sep 2022.

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References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
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  6. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 501
  7. 2014Semi-Supervised Learning with Deep Generative Modelscited by 496
  8. 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++cited by 485
  9. 2020Action2Motion: Conditioned Generation of 3D Human Motionscited by 441
  10. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  11. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  12. 2020Learning to Generate Diverse Dance Motions with Transformercited by 85
2 more
  1. 2019Beyond Imitation: Generative and Variational Choreography via Machine Learningcited by 6
  2. 2017Creative Robot Dance with Variational Encodercited by 3

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