  Generative models have surged in popularity recently due to their ability to
produce high-quality images and video. However, steering these models to
produce images with specific attributes and precise control remains
challenging. Humans, particularly their faces, are central to content
generation due to their ability to convey rich expressions and intent. Current
generative models mostly generate flat neutral expressions and characterless
smiles without authenticity. Other basic expressions like anger are possible,
but are limited to the stereotypical expression, while other unconventional
facial expressions like doubtful are difficult to reliably generate.
  In this work, we propose the use of AUs (action units) for facial expression
control in face generation. AUs describe individual facial muscle movements
based on facial anatomy, allowing precise and localized control over the
intensity of facial movements. By combining different action units, we unlock
the ability to create unconventional facial expressions that go beyond typical
emotional models, enabling nuanced and authentic reactions reflective of
real-world expressions. The proposed method can be seamlessly integrated with
both text and image prompts using adapters, offering precise and intuitive
control of the generated results. Code and dataset are available in
{https://github.com/tvaranka/fineface}.
