Text-Based Customized AI Video Generation with Real-Time AI Models
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Solution Overview
Problem
Existing methods for generating video content are inefficient and lack personalization, failing to adapt to user preferences and specific content creation requirements, particularly in areas like marketing and education.
Innovation Solution
A system utilizing generative AI models to analyze user input, learn user preferences, and generate customized video content by selecting templates, creating scripts, and designing scene layouts tailored to specific styles and formats, including emotional and thematic elements, while adhering to industry standards and legal guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used for generating video content, then the process is simple, but the efficiency is low and personalization is lacking
Solution Approach 1:
The video generation system is divided into multiple specialized modules: text analysis module, template selection module, script creation module, scene layout design module, and video assembly module. Each module handles a specific aspect of video generation, allowing parallel processing and improving overall efficiency while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
A generative AI model acts as an intermediary between user text input and video output. The AI model translates textual descriptions into structured video plans, selects appropriate templates, and coordinates between different modules, thereby simplifying the overall system architecture while enabling sophisticated personalized video generation.
2Adaptability or versatility
If generic video generation methods are used, then the system is easy to operate, but the content lacks personalization and user preference adaptation
Solution Approach 1:
The system automatically analyzes user text input, extracts preferences and requirements, selects appropriate templates, and generates personalized video content without requiring users to manually configure multiple parameters. The AI model learns from user feedback and continuously adapts to individual preferences, making the system self-improving while maintaining ease of operation.
Solution Approach 2:
The system pre-processes user text input to identify key preferences, requirements, and stylistic elements before video generation begins. Templates and resources are pre-organized and tagged for quick retrieval based on analyzed preferences, enabling rapid personalized video generation while keeping the user interface simple.
3Loss of time
If manual video creation processes are used, then quality control is straightforward, but the production time is excessive
Solution Approach 1:
The system incorporates multiple feedback mechanisms: automated quality checks after script generation, template suitability assessment, scene layout validation, and final video review. User feedback on generated videos is captured and used to refine future generations. This multi-stage feedback loop ensures high content quality while maintaining rapid automated production speeds.
Solution Approach 2:
Manual video creation tasks are replaced by automated AI processes: text analysis replaces manual requirement gathering, template selection replaces manual template browsing, script generation replaces manual writing, and video assembly replaces manual editing. This substitution dramatically reduces production time while maintaining quality through algorithmic consistency and automated quality assurance.
Data Source
AI summary
A method for generating customized AI model for generating video, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the method including the steps of identifying from user text of new category for generating video by analyzing context, comparing to known categories of video by using AI model to identify known or new category; generating in real time personal/customized AI model for new category by learning subject by third party AI large language acting as an expert for generating new AI model for new category trained by data of different types of videos and use case, for different subjects and video structure; generating in real time personal/customized video by applying the generated AI model of the new category using the user original prompt.