  As a sub-domain of text-to-image synthesis, text-to-face generation has huge
potentials in public safety domain. With lack of dataset, there are almost no
related research focusing on text-to-face synthesis. In this paper, we propose
a fully-trained Generative Adversarial Network (FTGAN) that trains the text
encoder and image decoder at the same time for fine-grained text-to-face
generation. With a novel fully-trained generative network, FTGAN can synthesize
higher-quality images and urge the outputs of the FTGAN are more relevant to
the input sentences. In addition, we build a dataset called SCU-Text2face for
text-to-face synthesis. Through extensive experiments, the FTGAN shows its
superiority in boosting both generated images' quality and similarity to the
input descriptions. The proposed FTGAN outperforms the previous state of the
art, boosting the best reported Inception Score to 4.63 on the CUB dataset. On
SCU-text2face, the face images generated by our proposed FTGAN just based on
the input descriptions is of average 59% similarity to the ground-truth, which
set a baseline for text-to-face synthesis.
