Text-to-Video Prompt Segmentation for Coherent Scene Generation
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Solution Overview
Problem
Existing AI video generation technologies struggle to effectively convert text into coherent and nuanced video representations, lacking the ability to capture the detailed actions, settings, and emotional nuances present in written content.
Innovation Solution
A method and system that splits text into scenes and beats, generating prompts for an AI video generating system to produce videos that include audio, using natural language processing to identify key elements like subject, setting, action, and atmosphere, and combining these elements into a holistic video representation, with optional user interaction feedback for refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If text is converted directly to video using existing AI video generation technologies, then the conversion process is simple, but the video representation lacks coherence and fails to capture detailed actions, settings, and emotional nuances
Solution Approach 1:
The patent segments text into hierarchical units (scenes, shots, beats) to enable precise control over video generation. By breaking down text into discrete narrative units with specific attributes (action, setting, emotion), the system achieves coherent and accurate video representations while managing complexity through structured processing
Solution Approach 2:
The patent performs preliminary text analysis to identify and structure narrative elements (scenes, shots, beats) before video generation. This preprocessing step extracts key attributes (main subject, setting, action, emotion) that guide subsequent AI video generation, ensuring accuracy without requiring complex real-time processing during rendering
2Manufacturing precision
If detailed text analysis is performed to capture actions, settings, and emotional nuances, then video representation accuracy improves, but processing time increases
Solution Approach 1:
The patent divides text processing into parallel segments (scene identification, shot breakdown, beat analysis) that can be processed independently and concurrently. This segmentation allows detailed narrative analysis without sequential bottlenecks, capturing comprehensive details while reducing overall processing time through parallel computation
Solution Approach 2:
The patent focuses processing on critical narrative elements (key actions, setting changes, emotional beats) rather than analyzing every text component equally. By identifying and prioritizing essential elements that drive video generation, the system achieves high narrative accuracy without the computational overhead of exhaustive text processing
3Manufacturing precision
If AI generates separate videos for each text segment, then video detail and coherence improve, but the number of generated videos and combining complexity increases
Solution Approach 1:
The patent segments video generation into discrete units corresponding to text beats, allowing parallel generation of multiple video segments. Each segment is generated with specific attributes (subject, setting, action) that ensure coherence, while the segmentation enables efficient parallel processing and systematic assembly into the final video
Solution Approach 2:
The patent implements feedback mechanisms where generated video segments are evaluated for coherence and consistency with source text. This feedback loop allows automatic adjustment of generation parameters and seamless stitching of segments, maintaining high coherence while optimizing the assembly process to prevent exponential complexity growth
Data Source
AI summary
Embodiments herein relate to generating a video representation text from an article, story, book, magazine, etc. Prompts can be generated to instruct the AI system to output an audio/visual representation of the text. Generating a prompt can include indicating the main subject of the text, the setting of the text, the action performed in the text, among other things. All text from the story can be represented in the video representation. For example, a first group text can be narrated overlaying the video, and a different group of different text can be audibly spoken by AI generated characters. Additionally, a user's interaction with the video can be monitored, and the prompt generation technique can be updated based on the user's interaction.