  The domain of 3D talking head generation has witnessed significant progress
in recent years. A notable challenge in this field consists in blending
speech-related motions with expression dynamics, which is primarily caused by
the lack of comprehensive 3D datasets that combine diversity in spoken
sentences with a variety of facial expressions. Whereas literature works
attempted to exploit 2D video data and parametric 3D models as a workaround,
these still show limitations when jointly modeling the two motions. In this
work, we address this problem from a different perspective, and propose an
innovative data-driven technique that we used for creating a synthetic dataset,
called EmoVOCA, obtained by combining a collection of inexpressive 3D talking
heads and a set of 3D expressive sequences. To demonstrate the advantages of
this approach, and the quality of the dataset, we then designed and trained an
emotional 3D talking head generator that accepts a 3D face, an audio file, an
emotion label, and an intensity value as inputs, and learns to animate the
audio-synchronized lip movements with expressive traits of the face.
Comprehensive experiments, both quantitative and qualitative, using our data
and generator evidence superior ability in synthesizing convincing animations,
when compared with the best performing methods in the literature. Our code and
pre-trained model will be made available.
