Pre-Capture Object Removal With Real-Time Content-Aware Fill
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Solution Overview
Problem
Conventional digital image editing systems require post-capture editing, which is inefficient, inflexible, and often results in unrealistic content replacement for removed objects due to limited context.
Innovation Solution
A pre-capture object removal system that uses machine learning models to detect, segment, and remove objects from an image stream in real-time, allowing users to preview and edit images before capture, and fill the removed objects with context-aware content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If post-capture editing is used to remove objects, then object removal can be performed, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs object removal and content generation in advance during the capture phase rather than after capture. The machine learning model detects and removes objects from the image stream before the photo is taken, allowing users to preview the result and capture the final image directly without post-processing time loss.
2Measurement precision
If conventional editing systems are used, then basic editing operations are available, but accuracy and flexibility are limited
Solution Approach 1:
The system introduces a machine learning model as an intermediary between object detection and removal. This intermediary enables sophisticated content-aware filling by analyzing surrounding image context, textures, and patterns to generate realistic replacement content, significantly improving both accuracy and flexibility compared to conventional editing tools.
Solution Approach 2:
The system provides real-time feedback by displaying the image stream with objects removed and holes filled during the capture phase. Users can preview the editing result before capturing the final image, allowing them to adjust the camera angle or lighting if needed, thus improving both accuracy and flexibility.
3Reliability
If objects are removed with limited context, then removal can be performed, but content replacement becomes unrealistic
Solution Approach 1:
The system performs content-aware filling during the capture phase when the full scene context is still available. The machine learning model has access to the complete image stream data, allowing it to analyze surrounding textures, patterns, and lighting conditions to generate realistic replacement content before any cropping or processing occurs.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for removing objects from an image stream at capture time of a digital image. For example, the disclosed system contemporaneously detects and segments objects from a digital image stream being previewed in a camera viewfinder graphical user interface of a client device. The disclosed system removes selected objects from the image stream and fills a hole left by the removed object with a content aware fill. Moreover, the disclosed system displays the image stream with the removed object and content fill as the image stream is previewed by a user prior to capturing a digital image from the image stream.