Automated Video Editing Style Transfer Using Shot Motion Profiles
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Solution Overview
Problem
User-generated visual content often lacks professional editing due to the complexity and cost of traditional non-linear editing, resulting in unenhanced content despite the potential for improved storytelling through editing attributes like framing, camera motion, and audio.
Innovation Solution
A system that automatically detects editing style in source content, determines shot boundaries, and transfers this style to target content using object detection, motion analysis, and editing attribute measurement, enabling efficient non-linear editing without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional non-linear editing is used to enhance visual content, then storytelling quality is improved, but device complexity and cost increase
Solution Approach 1:
The system copies the editing style attributes (framing, camera motion, transitions, audio) from source content to target content, allowing professional-quality editing effects to be replicated without requiring professional editors or complex manual editing processes. The style transfer mechanism creates a copy of the stylistic characteristics that can be applied to user-generated content.
Solution Approach 2:
The patent replaces the mechanical system of manual editing operations with an automated computer vision and machine learning system. Instead of requiring editors to manually adjust framing, camera motion, and audio, the system automatically detects these attributes from source content and applies them to target content through algorithmic processing.
2Manufacturing precision
If professional editing software and skills are used, then content quality is improved, but ease of operation decreases
Solution Approach 1:
The system performs self-service by automatically analyzing source content to extract editing style attributes and applying them to target content without requiring user intervention. The automated detection of shot boundaries, framing, camera motion, and audio characteristics eliminates the need for users to have professional editing skills or manually configure editing parameters.
Solution Approach 2:
The system provides universal editing capabilities that work across different types of visual content and can be accessed by any user regardless of editing expertise. The style transfer framework is designed to handle various content formats and automatically adapt the editing attributes, making professional-quality editing universally accessible.
3Manufacturing precision
If manual editing is performed on vast amounts of visual content, then editing precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-processing the source content to extract and store editing style attributes before applying them to target content. The automated detection and analysis of framing, camera motion, transitions, and audio characteristics are performed in advance, creating a reusable style profile that can be quickly applied to multiple pieces of content without repeated manual analysis.
Solution Approach 2:
The system creates an inert processing environment where editing operations are automatically executed without human intervention. The computer vision and machine learning algorithms continuously process visual content, detecting shot boundaries and applying style transfers in an automated pipeline that eliminates the time-consuming back-and-forth of manual editing review and adjustment.
Data Source
AI summary
The present disclosure provides systems, methods, and computer program products for performing automated non-linear editing style transfer. A computer-implemented method may include determining one or more shot boundaries in a video, analyzing identified content in each of one or more shots in the video based on performing object detection, determining an editing style for each of the one or more shots in the video based at least in part on measuring motion across frames within the respective shots, determining a content segment to adjust from a set of target content based on analyzing the set of target content in view of the identified content and the determined editing style of a shot from the video, and automatically adjusting the content segment from the set of target content based at least in part on modifying the content segment with the determined editing style of the shot from the video.