AI Video Clip Generation for Narrative-Coherent Short Segments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers of streaming media often watch video content in short bursts, leading to a loss of narrative thread upon resuming, and there is a desire for condensed and segmented video content that maintains narrative coherence.
Innovation Solution
An AI-powered system classifies video content using NLP and image recognition to identify narrative elements, defines segments based on prioritization rules, and generates video clips with a duration less than the full content, tailored to user preferences and habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video content is streamed in full duration, then complete narrative is provided, but user time consumption increases and narrative coherence is lost when users pause and resume
Solution Approach 1:
The video content is divided into multiple short clips, each capturing a distinct narrative moment or scene. This segmentation allows users to consume content in manageable portions while maintaining narrative coherence within each clip, resolving the contradiction between providing complete narrative and reducing user time consumption.
Solution Approach 2:
Key narrative moments and essential scenes are extracted from the full video content to create condensed clips. This extraction process identifies and isolates the most important narrative elements, ensuring that users receive the essential story arc without watching the entire original content, thus reducing time consumption while preserving narrative coherence.
2Loss of time
If video content is condensed into shorter clips, then user time consumption is reduced, but narrative coherence may be compromised
Solution Approach 1:
The system performs preliminary analysis of the full video content to identify key narrative moments, scenes, and transitions before generating clips. This preliminary action ensures that the condensed clips maintain narrative coherence by strategically selecting and sequencing important moments, allowing users to consume content quickly without losing the story arc.
Solution Approach 2:
The system uses feedback from user viewing patterns and preferences to refine clip generation. By analyzing which narrative moments users typically engage with and how they consume content, the system adjusts clip selection and sequencing to better maintain narrative coherence while optimizing for reduced viewing time.
3Adaptability or versatility
If AI analysis is applied to identify narrative elements, then clip quality and personalization are improved, but system complexity increases
Solution Approach 1:
The system replaces manual content analysis and curation with automated AI-powered analysis. Machine learning models analyze video content to identify narrative elements, scenes, and moments, eliminating the need for manual review while enabling personalized clip generation. This substitution reduces operational complexity while enhancing adaptability and personalization capabilities.
Data Source
AI summary
A system, a method and a process are for AI-powered generation and delivery of video clips. The processor is configured to: classify a first video content item including a first video file comprising video frames associated with timestamps and a first subtitle file comprising subtitle text associated with the timestamps, wherein the first video content item is classified with narrative classifiers by: executing a natural language processing (NLP) model with the first subtitle file as input, the NLP model including a dialogue analysis for identifying first narrative elements from dialogue included in the first subtitle file and associating the first narrative elements with first timestamps, executing an image recognition model with the first video file as input, the image recognition model including an object identification analysis for identifying second narrative elements from objects or persons portrayed in the video frames of the first video file and associating the second narrative elements with second timestamps, combining a first output of the NLP model with a second output of the image recognition model, and generating a first set of timestamps associated with the narrative classifiers; define one or more segments within the first video content item, each segment comprising a starting timestamp and an ending timestamp defining a duration and having one or more of the narrative classifiers associated therewith; and generate a video clip including one or more of the segments based on prioritization rules in which some narrative classifiers are associated with a priority for inclusion in the video clip, the one or more segments selected for inclusion in the video clip so that a combined duration of the one or more segments is less than a set time value, the set time value being less than a full duration of the first video content item.