@InProceedings{FerreiraMartNasc:2021:SyReHu,
               author = "Ferreira, Jo{\~a}o Pedro Moreira and Martins, Renato and 
                         Nascimento, Erickson Rangel",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universit{\'e} 
                         Bourgogne Franche-Comt{\'e}} and {Universidade Federal de Minas 
                         Gerais}",
                title = "Synthesizing realistic human dance motions conditioned by musical 
                         data using graph convolutional networks",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Human motion generation, sound and dance processing, multimodal 
                         learning, conditional adversarial nets, graph convolutional neural 
                         networks.",
             abstract = "Learning to move naturally from music, i.e., to dance, is one of 
                         the most complex motions humans often perform effortlessly. 
                         Synthesizing human motion through learning techniques is becoming 
                         an increasingly popular approach to alleviating the requirement of 
                         new data capture to produce animations. Most approaches, 
                         addressing the problem of automatic dance motion synthesis with 
                         classical convolutional and recursive neural models, undergo 
                         training and variability issues due to the non-Euclidean geometry 
                         of the motion manifold structure. In this thesis, we design a 
                         novel method based on graph convolutional networks, that overcome 
                         the aforementioned issues, to tackle the problem of automatic 
                         dance generation from audio information. Our method uses an 
                         adversarial learning scheme conditioned on the input music audios 
                         to create natural motions preserving the key movements of 
                         different music styles. We also collected, annotated and made 
                         publicly available a novel multimodal dataset with paired audio, 
                         motion data and videos of people dancing three different music 
                         styles, as a common ground to evaluate dance generation 
                         approaches. The results suggest that the proposed GCN model 
                         outperforms the state-of-the-art dance generation method 
                         conditioned on music in different experiments. Moreover, our 
                         graph-convolutional approach is simpler, easier to be trained, and 
                         capable of generating more realistic motion styles regarding 
                         qualitative and different quantitative metrics. It also presents a 
                         visual movement perceptual quality comparable to real motion data. 
                         The dataset, source code, and qualitative results are available on 
                         the project's webpage: 
                         https://verlab.github.io/Learning2Dance_CAG_2020/.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45DAPHE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45DAPHE",
           targetfile = "wtd-sibgrapi-joao.pdf",
        urlaccessdate = "2024, May 06"
}