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Text to Face Generation with StyleGAN2

Text-to-Face Generation with StyleGAN2 Authors D. M. A. Ayanthi and Sarasi Munasinghe, University of Ruhuna, Sri Lanka Abstract Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation(T2F) is a sub-domain of Text-to-Image generation(T2I) that is more challenging due to the complexity and variation of facial attributes. It has a number of applications mainly in the domain of public safety. Even though several models are available for T2F, there is still the need to improve the image quality and the semantic alignment. In this research, we propose a novel framework, to generate facial images that are well-aligned with the input descriptions. Our framework utilizes the highresolution face generator, StyleGAN2, and explores the possibility of using it in T2F. Here, we embed text in the input latent space of StyleGAN2 using BERT embeddings and oversee the generation of facial images using text descriptions. We trained our framework on attributebased descriptions to generate images of 1024x1024 in resolution. The images generated exhibit a 57% similarity to the ground truth images, with a face semantic distance of 0.92, outperforming state-of-the-artwork. The generated images have a FID score of 118.097 and the experimental results show that our model generates promising images. Keywords Text-to-Face Generation, StyleGAN2, High-Resolution, Semantic Alignment, Perceptual Loss. Full Text : https://aircconline.com/csit/papers/v... Abstract URL : https://aircconline.com/csit/abstract... Volume URL : https://airccse.org/csit/V12N08.html #Text-to-FaceGeneration #StyleGAN2 #High-Resolution #SemanticAlignment #PerceptualLoss 00:00 Objective 00:30 Background Study 06:20 Related Work 08:10 Problem Definition 08:40 Research Objectives 09:20 Methodology 12:30 Text Encoder 15:30 Image Genattor 16:45 Perceptual Loss Model 18:20 Evalution Method 20:00 Results 26:45 Conclusion and Future work