Semantic Segmentation for Automatic Video Transitions
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Solution Overview
Problem
Current video editing technologies lack the ability to automatically generate video transitions and visual effects based on semantic segmentation, which limits the efficiency and creativity in video production.
Innovation Solution
A method and system that utilize visual analysis to automatically select and combine media entities, perform semantic segmentation, and generate masking transitions or visual effects by differentiating between foreground and background objects, allowing for the creation of sophisticated video productions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic video editing is implemented without semantic segmentation, then productivity is improved through automation, but the quality and appropriateness of transitions and effects deteriorate due to lack of semantic understanding
Solution Approach 1:
The video content is segmented into foreground objects and background regions through semantic segmentation. This allows the system to apply different transitions and effects to different semantic regions independently, enabling sophisticated composite transitions that maintain high quality while being automatically generated.
Solution Approach 2:
The system changes the parameter of semantic understanding by integrating semantic segmentation results into the transition generation process. By using semantic labels and segmentation masks as additional parameters, the system can make intelligent decisions about transition types and parameters, resolving the contradiction between automation and quality.
2Manufacturing precision
If semantic segmentation is integrated into automatic video editing, then the quality and appropriateness of transitions improve through semantic understanding, but device complexity increases
Solution Approach 1:
The semantic segmentation module serves multiple functions: it provides semantic labels for content understanding, generates segmentation masks for transition application, and enables both simple and complex transition types. This multi-functionality reduces the need for separate specialized modules, managing complexity while improving transition quality.
Solution Approach 2:
The semantic segmentation results act as an intermediary between the raw video content and the transition generation process. This intermediary layer provides structured semantic information that simplifies the decision-making process for transition selection and parameter adjustment, managing system complexity while enhancing transition quality.
3Adaptability or versatility
If semantic segmentation is used to generate transitions, then adaptability of transitions to different content types improves, but processing time increases
Solution Approach 1:
Semantic segmentation is performed as a preliminary action during the video analysis phase, before transition generation. The segmentation results and semantic labels are cached and reused during transition creation, allowing the system to adapt to different content types without repeating the computationally intensive segmentation process, thus reducing overall processing time.
Solution Approach 2:
The system applies semantic segmentation at the appropriate level of detail - using full instance-based segmentation for complex scenes requiring high adaptability, and class-based segmentation for simpler transitions. This selective application of segmentation granularity optimizes the balance between adaptability and processing time.
Data Source
AI summary
A method and a system for automatic video production are provided herein. The method may include the following steps: obtaining a set of media entities, wherein at least one of the media entities comprises a background and at least one foreground object; automatically analyzing the media entities using visual analysis; automatically selecting at least two visual portions, based on the visual analysis; computing, for at least one of the visual portions, semantic segmentation indicative of a support of the at least one foreground object, based on the visual analysis; generating at least one visual effect in which the foreground object and the background undergo two different visual operations; and generating a video production by combining a plurality of the visual portions into one video production, while including the at least one visual effect in the video production.