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Abstract

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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.

Details

1009240
Subject
Annotations;
Dance
Identifier / keyword
Machine Learning
URL
http://arxiv.org/abs/2209.10010
Title
Intentional Choreography with Semi-Supervised Recurrent VAEs
Author
Papillon, Mathilde; Pettee, Mariel; Miolane, Nina
Publication title
arXiv.org; Ithaca
Publication year
2022
Publication date
Sep 20, 2022
Section
Computer Science
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
Publication subject
Biology, Statistics, Physics, Mathematics, Engineering--Electrical Engineering, Computers--Computer Engineering, Business And Economics--Banking And Finance
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2022-09-22
Milestone dates
2022-09-20 (Submission v1)
Publication history
 
 
   First posting date
22 Sep 2022
ProQuest document ID
2716768243
Document URL
https://www.proquest.com/working-papers/intentional-choreography-with-semi-supervised/docview/2716768243/se-2?accountid=208611
Full text outside of ProQuest
http://arxiv.org/abs/2209.10010
Copyright
© 2022. This work is published under http://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2022-09-23
Database
ProQuest One Academic