Intelligent Video Reframing via Region-of-Interest Camera Paths
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Solution Overview
Problem
Conventional methods for converting videos from one aspect ratio to another fail to maintain regions of interest and composition, making them inefficient and requiring manual expertise or relying on center cropping that ignores key frame parts.
Innovation Solution
A video reframing system that generates a camera path based on identified regions of interest to optimize sub-crop placement, minimizing camera movement while preserving the original composition and visibility of regions of interest, ensuring the artistic vision is maintained throughout the reframing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to maintain regions of interest in cropped video, then the quality of reframed video is improved, but the time required for conversion increases significantly
Solution Approach 1:
The system automatically identifies regions of interest and generates camera paths without requiring manual user input for each frame. The algorithm self-adjusts to maintain composition quality while achieving automatic conversion, eliminating the need for manual frame-by-frame analysis while preserving video quality.
Solution Approach 2:
The system changes the parameter of camera movement by generating optimized camera paths that balance region of interest visibility with minimal unnecessary movement. This parameter optimization allows automatic processing to achieve results comparable to manual methods.
2Productivity
If automatic center cropping is used to decrease conversion time, then the speed of conversion is improved, but the regions of interest and composition are lost
Solution Approach 1:
The system performs preliminary analysis to identify regions of interest in each frame before generating the camera path. This advance preparation allows the automatic system to know where to focus the crop, ensuring regions of interest are maintained while still achieving fast automatic conversion without frame-by-frame manual processing.
Solution Approach 2:
The system uses dynamic camera paths that adjust the crop region frame-by-frame based on the position and movement of identified regions of interest. This dynamic approach allows the crop to follow the action in the video while maintaining composition quality, unlike static center cropping.
3Manufacturing precision
If manual video editing is performed to maintain composition, then the artistic vision is preserved, but the complexity and time requirement increase
Solution Approach 1:
The system replaces manual mechanical editing processes with an automated computational system that analyzes video content, identifies regions of interest, and generates optimized camera paths. This substitution maintains composition quality while eliminating the need for manual editing expertise and complex user interaction.
Solution Approach 2:
The system uses feedback from region of interest detection to continuously adjust camera path generation. By monitoring the position and importance of key elements in each frame, the system automatically adjusts the crop to maintain composition, providing the same quality as manual editing but through automated feedback loops rather than human judgment.
Data Source
AI summary
Embodiments of the present invention are directed towards reframing videos from one aspect ratio to another aspect ratio while maintaining visibility of regions of interest. A set of regions of interest are determined in frames in a video with a first aspect ratio. The set of regions of interest can be used to estimate an initial camera path. An optimal camera path is determined by leveraging the identified regions of interest using the initial camera path. Sub crops with a second aspect ratio different from the first aspect ratio of the video are identified. The sub crops are placed as designated using the optimal camera path to generate a cropped video with the second aspect ratio.