Text-Driven Video Assembly Using Reusable Shot Selection
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Solution Overview
Problem
Existing video generation methods, such as manual editing and real-person shooting based on scripts, are inefficient, labor-intensive, and costly, lacking replicability and scalability.
Innovation Solution
A method involving text segmentation, candidate material selection, and video generation using a pre-trained model to automatically generate videos from text information, utilizing a material library and advanced algorithms for efficient video creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual editing or real-person shooting methods are used to generate videos, then video quality can be controlled, but labor costs and time consumption increase significantly
Solution Approach 1:
The patent uses pre-shot video materials as templates that can be copied and reused for different video generation tasks. Instead of shooting new videos each time, the system selects and combines existing video clips from a material library that match the semantic content of input text, dramatically reducing production time while maintaining quality through careful selection and arrangement of pre-produced content
Solution Approach 2:
The patent performs preliminary actions by pre-shooting and storing various video materials in a material library before actual video generation is needed. Video clips are pre-categorized and tagged with semantic information, allowing rapid retrieval and assembly during video generation without requiring time-consuming shooting and editing processes for each new video
2Adaptability or versatility
If manual video editing methods are used, then video content can be customized, but labor intensity and costs increase
Solution Approach 1:
The system performs self-service by automatically understanding input text, selecting appropriate video materials, and assembling videos without requiring manual intervention. The automated video generation system processes text input, retrieves matching video clips from the material library based on semantic analysis, and combines them according to temporal and logical relationships, eliminating the need for manual video editing operations
Solution Approach 2:
The patent creates a universal video generation system that can handle various types of video production tasks through a single automated platform. The material library contains diverse video materials that can be used for different purposes, and the system can generate different types of videos by simply changing the input text, making the same system versatile for multiple video production scenarios
3Manufacturing precision
If traditional video production methods are used, then video quality can be maintained, but scalability and replicability are limited
Solution Approach 1:
The system enables scalable video production by copying and reusing standardized video materials from the material library. Once video clips are shot and processed, they can be copied and used across multiple video generation tasks, allowing the system to scale up production volume without proportionally increasing production costs or time investment
Solution Approach 2:
The patent segments video content into discrete, reusable clips that can be independently selected and combined. By breaking down video content into modular segments with clear semantic tags and temporal structures, the system can efficiently assemble different combinations of clips for various video generation tasks, enabling both quality control through selective assembly and scalability through modular reuse
Data Source
AI summary
Disclosed is a video generation method in the fields of deep learning, large models, natural language processing and computer vision. The video generation method includes: obtaining text information to be processed, and segmenting the text information to be processed into sentences to obtain target sentences; selecting video shots matching the target sentences respectively from video shots in a material library as candidate materials corresponding to the target sentences respectively; for the target sentences, performing the following processing respectively: determining a screening method corresponding to a target sentence according to a position of the target sentence in the text information to be processed, selecting a target material for generating a required target video from the candidate materials corresponding to the target sentence according to the screening method; and generating the target video based on the target materials corresponding to the target sentences, respectively.