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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T164100
DTEND;TZID=Asia/Tokyo:20241205T165300
UID:siggraphasia_SIGGRAPH Asia 2024_sess137_papers_484@linklings.com
SUMMARY:HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Sy
 nthesis and Manipulation
DESCRIPTION:Technical Papers\n\nAbdul Basit Anees (Koç University), Ahmet 
 Canberk Baykal (University of Cambridge), Muhammed Burak Kizil (Koç Univer
 sity), Duygu Ceylan (Adobe Research), Erkut Erdem (Hacettepe University), 
 and Aykut Erdem (Koç University)\n\nGenerative Adversarial Networks (GANs)
 , particularly StyleGAN and its variants, have demonstrated remarkable cap
 abilities in generating highly realistic images. Despite their success, ad
 apting 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 f
 ramework that significantly extends the capabilities of a pre-trained Styl
 eGAN 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 discrimi
 nator that enhances the alignment between generated images and target doma
 ins, ensuring superior image quality. Our approach demonstrates unpreceden
 ted 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 robustn
 ess and superior performance of our framework compared to existing methods
 .\n\nRegistration Category: Full Access, Full Access Supporter\n\nLanguage
  Format: English Language\n\nSession Chair: Michael Rubinstein (Google)
URL:https://asia.siggraph.org/2024/program/?id=papers_484&sess=sess137
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