Generative Artificial Intelligence for Visual Applications: Architectures, Applications, and Challenges
Keywords:
Deep learning, Diffusion models, Generative adversarial networks (GANs), Generative artificial intelligence, Variational autoencoders (VAEs), Visual applicationsAbstract
Generative artificial intelligence (generative AI) represents one of the most transformative advances in modern computing, especially in the domain of visual applications. Its ability to generate, reconstruct, and enhance visual content has redefined the boundaries of creativity, automation, and perception. This study presents a systematic review of existing studies on generative AI in visual domains, focusing on three dominant architectures—Generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. The methodology involves a structured literature search across major databases, screening studies using defined inclusion criteria, and synthesizing results into thematic insights. The review identifies core technical principles, major datasets, evaluation metrics, and application areas such as image synthesis, video generation, and 3D content creation. Moreover, it discusses ongoing challenges related to reproducibility, computational cost, interpretability, and ethical considerations, including bias and misinformation. The paper concludes with emerging trends such as controllable generation, multimodal fusion, and sustainability-oriented generative modeling, aiming to guide future research toward responsible and transparent visual AI.
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