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Animating still face images with deep generative models using a speech inputsignal is an active research topic and has seen important recent progress.However, much of the effort has been put into lip syncing and rendering qualitywhile the generation of natural head motion, let alone the audio-visualcorrelation between head motion and speech, has often been neglected. In thiswork, we propose a multi-scale audio-visual synchrony loss and a multi-scaleautoregressive GAN to better handle short and long-term correlation betweenspeech and the dynamics of the head and lips. In particular, we train a stackof syncer models on multimodal input pyramids and use these models as guidancein a multi-scale generator network to produce audio-aligned motion unfoldingover diverse time scales. Our generator operates in the facial landmark domain,which is a standard low-dimensional head representation. The experiments showsignificant improvements over the state of the art in head motion dynamicsquality and in multi-scale audio-visual synchrony both in the landmark domainand in the image domain.

A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation