Revolutionizing Visuals: The Role of Generative AI in Modern Image Generation

被引:16
作者
Bansal, Gaurang [1 ]
Nawal, Aditya [2 ]
Chamola, Vinay [3 ]
Herencsar, Norbert [4 ]
机构
[1] Natl Univ Singapore, Singapore, Singapore
[2] Microsoft, Hyderabad, India
[3] BITS Pilani, Pilani, India
[4] Brno Univ Technol, Brno, Czech Republic
关键词
Generative AI; LLMs; Image Generation; Computing; Multimedia;
D O I
10.1145/3689641
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traditional multimedia experiences are undergoing a transformation as generative AI integration fosters enhanced creative workflows, streamlines content creation processes, and unlocks the potential for entirely new forms of multimedia storytelling. It has potential to generate captivating visuals to accompany a documentary based solely on historical text descriptions, or creating personalized and interactive multimedia experiences tailored to individual user preferences. From the high-resolution cameras in our smartphones to the immersive experiences offered by the latest technologies, the impact of generative imaging undeniable. This study delves into the burgeoning field of generative AI, with a focus on its revolutionary impact on image generation. It explores the background of traditional imaging in consumer electronics and the motivations for integrating AI, leading to enhanced capabilities in various applications. The research critically examines current advancements in state-of-the-art technologies like DALL-E 2, Craiyon, Stable Diffusion, Imagen, Jasper, NightCafe, and Deep AI, assessing their performance on parameters such as image quality, diversity, and efficiency. It also addresses the limitations and ethical challenges posed by this integration, balancing creative autonomy with AI automation. The novelty of this work lies in its comprehensive analysis and comparison of these AI systems, providing insightful results that highlight both their strengths and areas for improvement. The conclusion underscores the transformative potential of generative AI in image generation, paving the way for future research and development to further enhance and refine these technologies. This article serves as a critical guide for understanding the current landscape and future prospects of AI-driven image creation, offering a glimpse into the evolving synergy between human creativity and artificial intelligence.
引用
收藏
页数:22
相关论文
共 26 条
[1]   Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review [J].
Apostolopoulos, Ioannis D. ;
Papathanasiou, Nikolaos D. ;
Apostolopoulos, Dimitris J. ;
Panayiotakis, George S. .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 49 (11) :3717-3739
[2]   Digital image processing techniques for detecting, quantifying and classifying plant diseases [J].
Arnal Barbedo, Jayme Garcia .
SPRINGERPLUS, 2013, 2 :1-12
[3]   Typology of Risks of Generative Text-to-Image Models [J].
Bird, Charlotte ;
Ungless, Eddie L. ;
Kasirzadeh, Atoosa .
PROCEEDINGS OF THE 2023 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2023, 2023, :396-410
[4]   Understanding and Creating Art with AI: Review and Outlook [J].
Cetinic, Eva ;
She, James .
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2022, 18 (02)
[5]  
Chan YHE., 2020, P 38 ECAADE C, P299
[6]   Digital image steganography: Survey and analysis of current methods [J].
Cheddad, Abbas ;
Condell, Joan ;
Curran, Kevin ;
Mc Kevitt, Paul .
SIGNAL PROCESSING, 2010, 90 (03) :727-752
[7]   Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models [J].
Chefer, Hila ;
Alaluf, Yuval ;
Vinker, Yael ;
Wolf, Lior ;
Cohen-Or, Daniel .
ACM TRANSACTIONS ON GRAPHICS, 2023, 42 (04)
[8]   Structure-Aware Deep Learning for Product Image Classification [J].
Chen, Zhineng ;
Al, Shanshan ;
Jia, Caiyan .
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2019, 15 (01)
[9]  
Daras G., 2022, arXiv
[10]   Art and the science of generative AI Understanding shifts in creative work will help guide AI's impact on the media ecosystem [J].
Epstein, Ziv ;
Hertzmann, Aaron .
SCIENCE, 2023, 380 (6650) :1110-1111