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Synthesizing human motion through learning techniques is becoming anincreasingly popular approach to alleviating the requirement of new datacapture to produce animations. Learning to move naturally from music, i.e., todance, is one of the more complex motions humans often perform effortlessly.Each dance movement is unique, yet such movements maintain the corecharacteristics of the dance style. Most approaches addressing this problemwith classical convolutional and recursive neural models undergo training andvariability issues due to the non-Euclidean geometry of the motion manifoldstructure.In this paper, we design a novel method based on graph convolutionalnetworks to tackle the problem of automatic dance generation from audioinformation. Our method uses an adversarial learning scheme conditioned on theinput music audios to create natural motions preserving the key movements ofdifferent music styles. We evaluate our method with three quantitative metricsof generative methods and a user study. The results suggest that the proposedGCN model outperforms the state-of-the-art dance generation method conditionedon music in different experiments. Moreover, our graph-convolutional approachis simpler, easier to be trained, and capable of generating more realisticmotion styles regarding qualitative and different quantitative metrics. It alsopresented a visual movement perceptual quality comparable to real motion data.

Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio