Web: http://arxiv.org/abs/2206.08010

June 17, 2022, 1:13 a.m. | Sigal Raab, Inbal Leibovitch, Peizhuo Li, Kfir Aberman, Olga Sorkine-Hornung, Daniel Cohen-Or

cs.CV updates on arXiv.org arxiv.org

The emergence of neural networks has revolutionized the field of motion
synthesis. Yet, learning to unconditionally synthesize motions from a given
distribution remains a challenging task, especially when the motions are highly
diverse. We present MoDi, an unconditional generative model that synthesizes
diverse motions. Our model is trained in a completely unsupervised setting from
a diverse, unstructured and unlabeled motion dataset and yields a well-behaved,
highly semantic latent space. The design of our model follows the prolific
architecture of StyleGAN …

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