{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generative-adversarial-network-for-text-to","title":"Generative Adversarial Network for Text-to-Face Synthesis and Manipulation with Pretrained BERT Model","arxiv_id":null,"date":"2022-01-12","proceeding":"FG 2022 1","authors":["Yutong Zhou，Nobutaka Shimada"],"abstract":"This work proposes a cyclic generative adversarial network with spatial-wise and channel-wise attention modules for text-to-face synthesis and manipulation. Then, we explore the pre-trained transformer-based BERT model to obtain text embedding. Furthermore, dual-layer perceptual loss and SSIM loss are introduced to reinforce the delicate features and preserve facial identity during the manipulation task. Additionally, we adopt a novel Flickr-Faces-HQ with Text descriptions (FFHQ-Text) dataset with numerous facial attribute annotations to advance the development of the text-to-face task. In particular, by introducing the StyleGAN encoder for learning latent representations to our proposed post-processing method, we demonstrate that even training on a smaller text-to-face dataset can synthesize more realistic images. Experimental results reveal the effectiveness of our approach, which generates photo-realistic facial images, edits the specific facial attribute with the correlated keywords manipulation, outperforms previous state-of-the-art methods both in quality and quantity, and suggests promising future directions.","url_abs":"https://ieeexplore.ieee.org/document/9666791","url_pdf":"https://ieeexplore.ieee.org/document/9666791","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"face-generation","task_name":"Face Generation"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"text-to-face-generation","task_name":"Text-to-Face Generation"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"adaptive-instance-normalization","method_name":"Adaptive Instance Normalization"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"r1-regularization","method_name":"R1 Regularization"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[{"slug":"ffhq-text","name":"FFHQ-Text","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}