by Louis Airale, Xavier Alameda-Pineda, Stéphane Lathuilière, and Dominique Vaufreydaz
ACM Transactions on Multimedia Tools and Applications
Abstract: We address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space. Deviating from talking head generation conditioned on audio that seldom emphasizes realistic head motions, we devise a GAN-based architecture that allows obtaining rich head motion sequences while avoiding known caveats associated with GANs.Namely, the autoregressive generation of incremental outputs ensures smooth trajectories, while a multi-scale discriminator on input pairs drives generation toward better handling of high and low-frequency signals and less mode collapse. We demonstrate experimentally the relevance of the proposed architecture and compare it with models that showed state-of-the-art performances on similar tasks.
