Video Person Segmentation Depth-Based Alpha Masking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing technologies struggle to achieve seamless transitions in the opacity of subjects within an image based on their estimated depth in the scene, often resulting in sudden and flickering transitions when subjects move between foreground and background.
Innovation Solution
The use of video image segmentation techniques, such as machine learning-based methods, to generate alpha masks that adjust the opacity of subjects based on their depth within the image, creating smooth transitions between fully opaque and fully transparent states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image processing methods are used to filter out subjects, then the filtering operation can be performed, but sudden and flickering transitions occur when subjects move between foreground and background
Solution Approach 1:
The patent changes the parameter of subject opacity from binary (visible/filtered) to continuous (0-255 alpha values), allowing smooth transitions. The system calculates alpha values based on depth map comparisons, where subjects within a threshold depth range receive intermediate opacity values rather than abrupt filtering, eliminating flickering effects.
Solution Approach 2:
The patent introduces dynamic opacity adjustment based on real-time depth analysis. Instead of static filtering, the system continuously evaluates subject depth positions and adjusts alpha mask values frame-by-frame, creating adaptive transitions that respond smoothly to subject movement between foreground and background regions.
2Reliability
If depth-based opacity adjustment is implemented, then seamless transitions can be achieved, but the processing complexity increases due to multiple mask generations and depth map comparisons
Solution Approach 1:
The patent merges multiple processing operations into an integrated pipeline: depth map generation, subject identification, alpha mask creation, and opacity adjustment are combined into a unified processing flow. This consolidation reduces redundant operations and streamlines the overall complexity despite the advanced functionality.
Solution Approach 2:
The system performs preliminary depth map generation and subject identification in advance before applying opacity adjustments. By pre-processing the depth information and identifying subjects early in the pipeline, the system prepares necessary data structures that simplify subsequent alpha mask generation and reduce real-time processing complexity.
Data Source
AI summary
Devices, methods, and non-transitory computer readable storage mediums for the seamless transition of subject into an image or scene are disclosed. The methods include obtaining a first image of a scene, the first image including at least a first subject. A first alpha mask is generated for the first image based on an image segmentation operation. A depth map is generated for the first image, and a foreground depth is determined for the scene. A second alpha mask is generated by modifying the first alpha mask based, at least in part, on comparisons between values in corresponding portions of the depth map and the determined foreground depth. The second alpha mask modifies an opacity of at least some portions of the first image corresponding to the location of the first subject.