Cross-Edited Video Generation Using Dynamic Programming and Feature Similarity
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Solution Overview
Problem
Creating cross-edited videos that seamlessly transition between multiple videos of the same artist recorded at different times and environments is time-consuming and relies heavily on the creator's discretion, requiring extensive frame-level comparison and trial-and-error.
Innovation Solution
An apparatus and method for generating cross-edited videos by extracting feature points from frames, calculating transition rewards based on similarity and elapsed time, and connecting video pieces using dynamic programming to create a seamless transition effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a creator manually creates cross-edited videos by comparing frames and applying transition effects, then the video transition quality is improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs automatic frame comparison, similarity calculation, and transition point selection without requiring manual creator intervention. The algorithm independently analyzes video frames, extracts feature points, calculates similarity metrics, and determines optimal transition points, enabling the system to serve itself in the video creation process.
Solution Approach 2:
The manual mechanical process of frame-by-frame comparison and transition effect application is replaced with an automated computational system. The system uses computer vision algorithms to extract feature points, calculate frame similarity using reward functions, and automatically select transition points, substituting manual creator work with automated image processing and machine learning techniques.
2Measurement precision
If manual frame-level comparison is performed to ensure accurate transitions, then the transition accuracy is improved, but the complexity of the creation process increases
Solution Approach 1:
The system extracts only the essential feature points from video frames that are necessary for determining transition accuracy, rather than analyzing all frame data. By extracting key feature points and using them to calculate similarity rewards, the system reduces the complexity of the creation process while maintaining high transition accuracy through targeted feature analysis.
3Manufacturing precision
If multiple trials and errors are conducted to achieve smooth transitions, then the transition quality is improved, but the productivity decreases
Solution Approach 1:
The system uses reward functions that provide feedback on frame similarity and transition quality. The first reward function evaluates similarity between consecutive frames, while the second reward function evaluates the overall video quality. This feedback mechanism guides the automatic selection of transition points, ensuring high transition quality without requiring multiple manual trials and errors.
Data Source
AI summary
Disclosed is an apparatus for generating a cross-edited video and a method of operating the apparatus. The method includes: obtaining a plurality of videos; sequentially generating video pieces such that one of the plurality of videos is played according to a timeline, based on a first transition reward that is based on a similarity between a video before a transition and a video after the transition in a specific frame and a continuous play time and on a second transition reward that is based on an elapsed time from a previous transition point; and generating a cross-edited video by connecting the video pieces.