  Text-to-Face (TTF) synthesis is a challenging task with great potential for
diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis
tasks, the textual description of faces can be much more complicated and
detailed due to the variety of facial attributes and the parsing of high
dimensional abstract natural language. In this paper, we propose a Text-to-Face
model that not only produces images in high resolution (1024x1024) with
text-to-image consistency, but also outputs multiple diverse faces to cover a
wide range of unspecified facial features in a natural way. By fine-tuning the
multi-label classifier and image encoder, our model obtains the vectors and
image embeddings which are used to transform the input noise vector sampled
from the normal distribution. Afterwards, the transformed noise vector is fed
into a pre-trained high-resolution image generator to produce a set of faces
with the desired facial attributes. We refer to our model as TTF-HD.
Experimental results show that TTF-HD generates high-quality faces with
state-of-the-art performance.
