  Head avatars animated by visual signals have gained popularity, particularly
in cross-driving synthesis where the driver differs from the animated
character, a challenging but highly practical approach. The recently presented
MegaPortraits model has demonstrated state-of-the-art results in this domain.
We conduct a deep examination and evaluation of this model, with a particular
focus on its latent space for facial expression descriptors, and uncover
several limitations with its ability to express intense face motions. To
address these limitations, we propose substantial changes in both training
pipeline and model architecture, to introduce our EMOPortraits model, where we:
  Enhance the model's capability to faithfully support intense, asymmetric face
expressions, setting a new state-of-the-art result in the emotion transfer
task, surpassing previous methods in both metrics and quality.
  Incorporate speech-driven mode to our model, achieving top-tier performance
in audio-driven facial animation, making it possible to drive source identity
through diverse modalities, including visual signal, audio, or a blend of both.
  We propose a novel multi-view video dataset featuring a wide range of intense
and asymmetric facial expressions, filling the gap with absence of such data in
existing datasets.
