Automatic Media Editing via Parameter Timeline
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Solution Overview
Problem
Existing media generation technologies lack efficiency in automatically creating temporally continuous media content from a set of media clips, often requiring extensive manual editing and time-consuming clip selection processes.
Innovation Solution
The method involves determining media files, optionally tagging them, determining generation parameters, amending a timeline based on these parameters, and rendering the timeline to automatically generate temporally continuous media content. This process utilizes a parameter timeline to guide clip selection and editing, ensuring continuity and coherence in the generated media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual editing and clip selection processes are used, then media generation can be performed with high quality control, but the process requires extensive time and effort
Solution Approach 1:
The system enables self-service media generation by automatically selecting clips, tagging them, and assembling them into coherent media content without requiring manual intervention. The AI model autonomously performs tasks that traditionally required human editors, including clip selection based on tags, temporal continuity verification, and narrative structure maintenance.
Solution Approach 2:
The patent replaces manual mechanical editing processes with an automated AI-based system. The mechanical process of manually selecting and arranging clips is substituted with an intelligent system that uses machine learning models to automatically generate media content, thereby eliminating the time-consuming nature of manual editing while maintaining or improving quality.
2Productivity
If automated media generation is implemented, then time and effort are reduced, but the system complexity increases
Solution Approach 1:
The automated media generation system is divided into distinct functional modules: a tagging module that assigns metadata to clips, a selection module that chooses appropriate clips based on tags and parameters, an assembly module that arranges clips temporally, and a rendering module that produces the final output. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The AI model serves multiple functions within the system: it performs tag generation, clip selection, temporal continuity verification, and narrative structure maintenance. This multi-functionality reduces the need for separate specialized systems for each task, thereby managing complexity while maintaining high productivity across multiple media generation operations.
3Reliability
If extensive manual editing is performed, then media quality and coherence are maintained, but productivity decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model continuously evaluates the coherence and quality of generated media content. Tags are assigned to clips based on their content, and the selection process uses these tags to ensure narrative consistency. The system provides feedback on temporal continuity and makes adjustments to maintain coherence while operating at high speed, thereby achieving both reliability and productivity.
Data Source
AI summary
Variants of the method can include: determining media files, optionally tagging a subset of the media files, determining generation parameters, amending the timeline according to the generation parameters, and/or optionally rendering the timeline. The method functions to automatically generate temporally continuous media content from a set of media clips. The usage of both tags and a set of parameter timelines enables the method to generate films which exhibit generative drift with respect to tags while maintaining overall control of the arc of the film.