A Detailed Review of Video Generation Using Textual Descriptions and Structural Guidance

Computer science and engineering

Artificial intelligence based multimedia generation has experienced rapid growth in recent years. Among different multimedia applications, text guided video generation has become an important research area because of its capability to automatically create videos from textual descriptions.Existing video generation systems often face challenges such as temporal inconsistency, motion instability, flickering artifacts,and poor structural preservation. Recent advancements in diffusion models have significantly improved video synthesis quality by introducing stable and realistic generation techniques. The framework discussed in this paper combines textual descriptions with structural guidance to improve temporal coherence and controllability during video generation. Structural guidance mechanisms such as depth maps help preserve scene geometry and maintain object consistency across consecutive frames.This review paper presents a comprehensive study of video generation using textual and structural guidance. The paper discusses diffusion models, latent diffusion architectures, temporal transformers, structural guidance mechanisms, methodology, applications, advantages, limitations, and future research directions. The study also compares several existing frameworks including Make-A-Video, Video Fusion, Text-to Video Zero, Imagen Video, and Control Video. The comparison highlights the importance of temporal learning and structural conditioning in improving realism, motion continuity, and controllability in modern video generation systems.

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