  Human face generation and editing represent an essential task in the era of
computer vision and the digital world. Recent studies have shown remarkable
progress in multi-modal face generation and editing, for instance, using face
segmentation to guide image generation. However, it may be challenging for some
users to create these conditioning modalities manually. Thus, we introduce
M3Face, a unified multi-modal multilingual framework for controllable face
generation and editing. This framework enables users to utilize only text input
to generate controlling modalities automatically, for instance, semantic
segmentation or facial landmarks, and subsequently generate face images. We
conduct extensive qualitative and quantitative experiments to showcase our
frameworks face generation and editing capabilities. Additionally, we propose
the M3CelebA Dataset, a large-scale multi-modal and multilingual face dataset
containing high-quality images, semantic segmentations, facial landmarks, and
different captions for each image in multiple languages. The code and the
dataset will be released upon publication.
