Text-to-Video Generation Using Semantic Data Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating video content from text are inefficient and cannot meet the growing demand for video content, as they lack automation and intelligence, resulting in fixed video patterns and limited applicability.
Innovation Solution
A video generation method that involves obtaining global and local semantic information from text, searching databases to retrieve candidate data, and matching text fragments with target data based on relevance, to generate coherent and consistent videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual video production methods are used, then video content can be created with basic quality control, but the production efficiency is low and cannot meet the growing demand for video content
Solution Approach 1:
The system enables automated video generation where the video production system itself performs semantic analysis, database searching, and video assembly without human intervention. The neural network automatically processes text input and generates corresponding video content, making the system self-sufficient in the video production workflow.
Solution Approach 2:
Manual mechanical video production processes are replaced with intelligent automated systems. The patent substitutes human operators with a neural network-based system that performs semantic understanding, data retrieval, and video compilation automatically, transforming mechanical manual operations into intelligent automated processing.
2Ease of operation
If simple text-to-video conversion is implemented, then the system is easy to operate, but the video content lacks coherence and consistency with the source text
Solution Approach 1:
The patent introduces semantic information as an intermediary between text input and video output. The system extracts semantic information from text, uses it to search and select relevant video data from databases, and ensures the generated video maintains semantic coherence with the source text, preventing information loss while keeping the system simple to use.
3Manufacturing precision
If comprehensive semantic analysis is performed on text to ensure video coherence, then video quality and consistency improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing text to extract semantic information before video generation begins. The neural network analyzes and structures the semantic content in advance, creating a organized representation that accelerates the subsequent video assembly process, thereby reducing overall production time while maintaining high quality.
Solution Approach 2:
The patent segments the video generation process into distinct stages: semantic information extraction, database searching based on semantic keywords, and video assembly. This segmentation allows each stage to be optimized independently, improving overall efficiency while maintaining comprehensive semantic analysis for video coherence.
4Productivity
If automated neural network-based video generation is implemented, then productivity and video quality improve, but the system complexity increases significantly
Solution Approach 1:
The patent employs a universal neural network system that performs multiple functions: semantic information extraction, database searching, and video generation. This multi-functional approach consolidates what would otherwise require separate specialized systems, reducing overall system complexity while maintaining high productivity and video quality.
Data Source
AI summary
A video generation method is provided. The video generation method includes: obtaining global semantic information and local semantic information of a text, where the local semantic information corresponds to a text fragment in the text; searching, based on the global semantic information, a database to obtain at least one first data corresponding to the global semantic information; searching, based on the local semantic information, the database to obtain at least one second data corresponding to the local semantic information; obtaining, based on the at least one first data and the at least one second data, a candidate data set; matching, based on a relevancy between each of at least one text fragment and corresponding candidate data in the candidate data set, target data for the at least one text fragment; and generating, based on the target data matched with each of the at least one text fragment, a video.