Contextual Video Transition Effects Using Flow-Guided Frame Generation
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Solution Overview
Problem
Individuals struggle with creating high-quality video edits due to the time-consuming and inflexible nature of traditional video editing processes, which often require manual selection and splicing of video segments, lacking seamless and contextually relevant transitions.
Innovation Solution
A method and electronic device utilizing a flow-guided video diffusion model to automatically generate contextual transition effects by detecting frame similarities, inserting masked frames, and updating pixel values to create seamless transitions based on detected motion and supplementary prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual video segment selection and splicing is used, then users can control the editing process, but the editing process becomes time-consuming and inefficient
Solution Approach 1:
The system performs automatic video segment selection, transition detection, and effect generation without requiring manual user intervention. The AI model analyzes video content, identifies appropriate transitions and effects, and generates edited videos autonomously, eliminating the time-consuming manual editing process while maintaining quality standards
Solution Approach 2:
The patent replaces manual mechanical video editing operations with an AI-based automated system. The machine learning model substitutes for human editors in analyzing video frames, detecting transitions, selecting effects, and generating final edits, thereby dramatically reducing editing time and increasing productivity
2Adaptability or versatility
If predefined transition options are used in traditional editing applications, then users can create basic transitions, but the transitions lack contextuality and flexibility
Solution Approach 1:
The system dynamically adjusts transition parameters based on the analyzed video content, including transition type, duration, timing, and visual effects. The AI model modifies these parameters adaptively to match the context of each video segment, creating customized transitions that are both contextually relevant and flexible to different video styles and genres
Solution Approach 2:
The patent implements a feedback loop where the AI model continuously analyzes video frames, detects transitions and content characteristics, and adjusts transition generation accordingly. This iterative feedback process ensures that transitions are optimized for each specific video context, enhancing adaptability while managing complexity through intelligent automation
3Measurement precision
If manual comparison of video frames is performed to capture necessary segments, then users can ensure accurate selection, but the process becomes labor-intensive and slow
Solution Approach 1:
The patent replaces manual frame-by-frame comparison with an AI-based automated detection system. The machine learning model analyzes video frames to identify transitions, significant changes, and appropriate segments for editing, achieving both high accuracy in segment selection and rapid processing speed, thereby eliminating the labor-intensive manual comparison process
Data Source
AI summary
A method may include obtaining a plurality of frames of at least one video; determining an occurrence of a change from a first event of the at least one video to a second event of the video; determining a location in the plurality of frames; inserting one or more masked frames at the location between at least one frame of the first event and at least one frame of the second event; determining at least one transition component present in the at least one frame of the first event and the at least one frame of the second event; determining a motion of pixels for the at least one transition component across the plurality of frames of the first event and the plurality of frames of the second event; providing at least one transition effect in the one or more masked frames between the first event and the second event.