Autoregressive GAN for Semantic Unconditional Head Motion Generation

Louis Airale (1, 2) , Xavier Alameda-Pineda (2) , Stéphane Lathuilière (3, 4, 5) , Dominique Vaufreydaz (1)

1 M-PSI - Multimodal Perception and Sociable Interaction
2 ROBOTLEARN - Vers des robots à l’intelligence sociale au travers de l’apprentissage, de la perception et de la commande
3 IP Paris - Institut Polytechnique de Paris
4 IDS - Département Images, Données, Signal
5 MM - Multimédia

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 puts emphasis on 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 with models that showed state-of-the-art performances on similar tasks.

Keywords: GAN; Head motion; Face landmarks

Domains: Computer Vision and Pattern Recognition [cs.CV]; Machine Learning [stat.ML]

Louis Airale, Xavier Alameda-Pineda, Stéphane Lathuilière, Dominique Vaufreydaz. Autoregressive GAN for Semantic Unconditional Head Motion Generation. 2022. ⟨hal-03833759v1⟩