Synthesizing Realistic Human Dance Motions Conditioned by Musical Data using Graph Convolutional Networks


Learning to move naturally from music, i.e., to dance, is one of the most complex motions humans often perform effortlessly. Existing techniques of automatic dance generation with classical CNN and RNN models undergo training and variability issues due to the non-Euclidean geometry of the motion manifold. We design a novel method based on GCNs to tackle the problem of automatic dance generation from audio. Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions. The results demonstrate that the proposed GCN model outperforms the state-of-the-art in different experiments. Visual results of the motion generation and explanation can be visualized through the link:
Palavras-chave: Human motion generation, Sound and dance processing, Multimodal learning, Conditional adversarial nets, Graph convolutional neural networks


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FERREIRA, João P. M.; MARTINS, Renato; NASCIMENTO, Erickson R.. Synthesizing Realistic Human Dance Motions Conditioned by Musical Data using Graph Convolutional Networks. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 34. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 79-84. ISSN 2763-8820. DOI: