Text-Driven Border Image Generation for Personalized Videos
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Solution Overview
Problem
Existing video generation methods suffer from poor effects due to lack of diversity and personalization in border images, leading to unimpressive video outcomes.
Innovation Solution
Extract text from the video to generate a border image associated with its content, using neural network models for image and natural language processing to create personalized and rich border images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If preset library images are used for border images, then the video generation process is simple and fast, but the border images lack diversity and personalization resulting in poor video effects
Solution Approach 1:
The system automatically generates border images by extracting text from video content and using the text as input for image generation, eliminating the need for manual selection from preset libraries. This self-service approach enables personalized border images that match each video's content while maintaining efficient automated processing
Solution Approach 2:
The patent changes the input parameters for border image generation from fixed preset options to dynamic text descriptions extracted from video content. This parameter change allows the border images to adapt to different video contents, providing diversity and personalization while maintaining automated generation efficiency
2Adaptability or versatility
If manual text input and image selection are required, then users have control over content, but the process becomes complex and time-consuming reducing video generation efficiency
Solution Approach 1:
The system performs text extraction and border image generation automatically without requiring user input or selection. The video content itself provides the text description, which then generates the appropriate border image, eliminating complex manual operations while maintaining content customization
Solution Approach 2:
The patent introduces text extraction as an intermediary step between video content and border image generation. This intermediary automatically converts video content into text descriptions, which then serve as input for generating personalized border images, simplifying the overall process while maintaining customization
3Productivity
If generic border images are used for all videos, then the processing is efficient and simple, but the video effects are poor due to lack of content matching
Solution Approach 1:
The patent applies local quality by generating unique border images tailored to each video's specific content characteristics. Instead of using a uniform approach for all videos, the system extracts content-specific text from each video and generates corresponding border images, ensuring high content matching accuracy while maintaining efficient automated processing
Solution Approach 2:
The system changes from fixed generic border images to dynamic content-matched border images by using text extraction as a variable input. This parameter change enables the border images to adapt to different video contents, achieving both processing efficiency through automation and high content matching accuracy
Data Source
AI summary
The present disclosure provides a method for generating a video having a border image and a device. The method includes: acquiring a first video; extracting, from the first video, a text for describing content in the first video; generating a border image based on the text, where the text is used to determine content of the border image; and compositing the first video with the border image to obtain a second video.