摘要
Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.
作者简介
Corresponding author:Shilong Li is currently pursuing a Ph.D.at Hangzhou Dianzi University under the supervision of Professor Qiang Zhao.The primary research focus is on panoramic image processing.Email:244060063@hdu.edu.cn;Qiang Zhao received the B.Eng.degree in software engineering and the Ph.D.degree in computer science and technology from Tianjin University,Tianjin,China,in 2009 and 2016,respectively.He is currently a Professor with the school of communication engineering,Hangzhou Dianzi University,Hangzhou,China.Before that,he was an Assistant Professor and Associate Professor with the Institute of Computing Technology,Chinese Academy of Sciences,Beijing,China.His main research interests include image based rendering,feature extraction,and panoramic image processing.
