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As a sub-domain of text-to-image synthesis, text-to-face generation has hugepotentials in public safety domain. With lack of dataset, there are almost norelated research focusing on text-to-face synthesis. In this paper, we proposea fully-trained Generative Adversarial Network (FTGAN) that trains the textencoder and image decoder at the same time for fine-grained text-to-facegeneration. With a novel fully-trained generative network, FTGAN can synthesizehigher-quality images and urge the outputs of the FTGAN are more relevant tothe input sentences. In addition, we build a dataset called SCU-Text2face fortext-to-face synthesis. Through extensive experiments, the FTGAN shows itssuperiority in boosting both generated images' quality and similarity to theinput descriptions. The proposed FTGAN outperforms the previous state of theart, boosting the best reported Inception Score to 4.63 on the CUB dataset. OnSCU-text2face, the face images generated by our proposed FTGAN just based onthe input descriptions is of average 59% similarity to the ground-truth, whichset a baseline for text-to-face synthesis.

FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation