AI Video Generation System Using NLP Template Selection
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Solution Overview
Problem
Current methods for generating video from text or voice instructions lack the ability to personalize and automate the video creation process effectively, failing to provide customized videos that meet specific user requirements and preferences.
Innovation Solution
A system and method that utilizes natural language processing and AI to analyze user instructions, select video templates, aggregate multimedia content, and generate personalized videos, allowing for customization and learning of user preferences to create tailored video products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated video generation from text is implemented, then productivity is improved, but manufacturing precision (video quality customization) deteriorates
Solution Approach 1:
The video generation process is divided into distinct segments: template selection, content aggregation, scene creation, and video assembly. Each segment can be independently optimized and controlled, allowing the system to maintain high automation while preserving customization quality through targeted adjustments in specific segments.
Solution Approach 2:
The system dynamically adjusts multiple parameters including video length, style, content type, and layout based on user input. By changing these parameters flexibly during generation, the system achieves both efficient automated processing and precise customization according to user requirements.
2Adaptability or versatility
If multiple video templates and content options are provided, then adaptability is improved, but device complexity deteriorates
Solution Approach 1:
A single unified system handles multiple functions including template selection, content aggregation from various sources, scene creation, and video assembly. This multi-functional approach provides broad adaptability while managing complexity through integration rather than separate specialized systems.
Solution Approach 2:
The system introduces intermediate representations such as standardized content objects, template frameworks, and scene graphs that mediate between user input and final video generation. These intermediaries simplify the complexity by providing structured layers that ease the transformation from diverse inputs to consistent outputs.
3Manufacturing precision
If AI learning and personalization are implemented, then product quality is improved, but loss of information (data processing requirements) deteriorates
Solution Approach 1:
The system implements feedback loops where user preferences and selections are captured, analyzed, and used to refine future video generations. This feedback mechanism enables personalization while managing data processing by focusing on extracting and utilizing only the essential preference information rather than processing all raw data.
Data Source
AI summary
The present invention discloses a method for generating video, to perform the steps of:receive entity instructions by text or voice using natural language;analyzing entity instructions for identifying technical and creative requirements including: style, context, content, type and properties of content objects, layout of video frames, order—sequence of disapplying content, functionality of objects;selecting video template of at least one scene based analysed instructions and all identified technical and creative requirements;exploring and aggregating content of text, image or video multimedia based on identified technical and creative requirements of the selected at least one template;generating new video by implementing selected or new video template using aggregated content wherein the generated video complies with all analyzed requirements.