Advances in Artificial Intelligence: A Review for the Creative Industries
Nantheera Anantrasirichai, Fan Zhang, David Bull
TL;DR
The paper surveys AI advances since 2022, emphasizing generative AI, LLMs, diffusion models, and INRs, and analyzes their integrated impact across the creative production pipeline. It highlights a shift from AI as a support tool to a core creative technology through unified frameworks, multimodal capabilities, and real-time 3D/immersive content generation. The authors discuss human-AI collaboration, limitations, and pressing challenges such as copyright, bias, compute demands, and regulatory needs. The study catalogs developments across content creation, information analysis, post-production, compression, and quality assessment, offering a cross-domain perspective on capabilities, constraints, and trajectories for researchers and practitioners.
Abstract
Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a systematic review of AI technologies that have emerged or matured since our 2022 review, examining their applications across content creation, information analysis, post-production enhancement, compression, and quality assessment. We document how transformers, LLMs, diffusion models, and implicit neural representations have established new capabilities in text-to-image/video generation, real-time 3D reconstruction, and unified multi-task frameworks-shifting AI from support tool to core creative technology. Beyond technological advances, we analyze the trend toward unified AI frameworks that integrate multiple creative tasks, replacing task-specific solutions. We critically examine the evolving role of human-AI collaboration, where human oversight remains essential for creative direction and mitigating AI hallucinations. Finally, we identify emerging challenges including copyright concerns, bias mitigation, computational demands, and the need for robust regulatory frameworks. This review provides researchers and practitioners with a comprehensive understanding of current AI capabilities, limitations, and future trajectories in creative applications.