HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation

Autor:	Anees, Abdul Basit, Baykal, Ahmet Canberk, Kizil, Muhammed Burak, Ceylan, Duygu, Erdem, Erkut, Erdem, Aykut
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Generative Adversarial Networks (GANs), particularly StyleGAN and its variants, have demonstrated remarkable capabilities in generating highly realistic images. Despite their success, adapting these models to diverse tasks such as domain adaptation, reference-guided synthesis, and text-guided manipulation with limited training data remains challenging. Towards this end, in this study, we present a novel framework that significantly extends the capabilities of a pre-trained StyleGAN by integrating CLIP space via hypernetworks. This integration allows dynamic adaptation of StyleGAN to new domains defined by reference images or textual descriptions. Additionally, we introduce a CLIP-guided discriminator that enhances the alignment between generated images and target domains, ensuring superior image quality. Our approach demonstrates unprecedented flexibility, enabling text-guided image manipulation without the need for text-specific training data and facilitating seamless style transfer. Comprehensive qualitative and quantitative evaluations confirm the robustness and superior performance of our framework compared to existing methods. Comment: Accepted for publication in SIGGRAPH Asia 2024. Project Website: https://cyberiada.github.io/HyperGAN-CLIP/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.12832 Zobrazit plný text záznamu View this record from Arxiv