Abstract :

The Exponential Growth Of Multimedia Platforms Has Created An Urgent Need For Automated, Scalable, And High-quality Video Production. Manual Video Creation Is Time-consuming And Requires Significant Expertise, Making It Challenging To Meet The Increasing Demand For Personalized And Dynamic Content. Intelligent Video Content Generation Using Deep Learning Proposes A Smart, Automated Approach To Create Video Content Using Advanced Neural Architectures Such As Generative Adversarial Networks (GANs), Vision Transformers (ViTs), Autoencoders, And Sequence-to-Sequence (Seq2Seq) Models.The System Processes User Inputs—text, Images, Or Short Prompts—and Uses Natural Language Processing (NLP) To Interpret Context And Generate A Semantic Scene Structure. Deep Generative Models Then Synthesize Video Frames With Realistic Motion, Smooth Transitions, And Coherent Visual Flow. Reinforcement Learning Further Enhances Frame Quality, Temporal Consistency, And Style Adaptation. This Framework Enables Rapid Creation Of Educational Videos, Marketing Clips, Animations, And Social Media Content With Minimal Human Effort.The Proposed Method Significantly Reduces Production Time And Cost While Improving Flexibility, Personalization, And Automation In Content Creation. It Represents A Major Advancement Toward Fully AI-driven Video Generation Technologies. Keywords- Deep Learning, Video Generation, Generative Adversarial Networks (GANs), Vision Transformers (ViTs), Text-to-Video Synthesis, Automated Content Creation, Natural Language Processing (NLP), Reinforcement Learning, Image-to-Video, Scene Generation

Published:

18-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

357-364

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite