Enhanced Panoramic Image Generation with GAN and CLIP Models  

作　　者：Shilong Li Qiang Zhao 

机构地区：[1]School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Journal of Beijing Institute of Technology》2025年第1期91-101,共11页北京理工大学学报(英文版)

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.

关 键 词：panoramic images environment texture generative adversarial networks(GANs) contrastive language-image pretraining(CLIP)model blender engine fine-grained control texture generation 

分 类 号：TN3[电子电信—物理电子学]