Advancements in Text-to-Video Creation through AI Models: A Comprehensive Review

Abstract

This review paper explores the dynamic landscape of text-to-video creation facilitated by Artificial Intelligence (AI) models. Examining the intersection of Natural Language Processing (NLP) and Computer Vision (CV), we delve into methodologies, challenges, and advancements shaping this evolving field. From traditional rule-based systems to advanced deep learning architectures like GPT and CLIP, the paper navigates through the diverse spectrum of AI models driving text-to-video synthesis. Challenges, such as context preservation and ethical considerations, are discussed, along with practical applications spanning entertainment, education, and communication. A comparative analysis evaluates the strengths and limitations of different models, offering insights for optimal usage. Looking forward, the paper explores future directions, emphasizing collaborative efforts and ethical considerations. This comprehensive review provides a valuable resource for those engaged in the intersection of NLP, CV, and AI-driven text-to-video technologies. This review delves into AI's role in text-to-video conversion, exploring methods like computer vision and natural language processing. It examines advancements, challenges, and implications in diverse sectors such as marketing, education, and entertainment.