Human motion generation with StyleGAN

Humanoid characters in games and videos move naturally as humans do in the real world. Recently, research has been conducted on generating natural motion using deep learning. Currently, there is a need to generate and control diverse natural motions. Therefore, this study proposes a model for generating human motions using StyleGAN, a deep generative model used in image generation. The performance of the proposed model was experimentally assessed on motion capture datasets; the results confirmed that the model could generate diverse and natural motions through random generation and intermediate latent variable interpolation. Additionally, experiments with style mixing indicated that the low-level layer could control the class of movements and the middle-level layer could control the positions of the arms and legs, posture, and body orientation.