Pre-Capture Camera Object Removal with Content-Aware Fill
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Solution Overview
Problem
Conventional digital image editing systems are inefficient, require post-capture editing, and often produce non-photorealistic results due to limited context for generating content to replace removed objects.
Innovation Solution
A pre-capture object removal system that utilizes machine learning models to detect, segment, and remove objects from an image stream in real-time, allowing users to preview and edit the image before capture, using context beyond the captured image to generate photorealistic content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional digital image editing systems are used to remove objects after capture, then object removal can be performed, but the editing process is inefficient and requires post-capture processing time
Solution Approach 1:
The system performs object detection, segmentation, and removal operations during the image capture phase rather than after capture. The machine learning models analyze the scene in real-time, identify objects to be removed, and generate replacement content before the image is finalized, thereby eliminating post-capture editing time and improving overall productivity.
2Manufacturing precision
If conventional editing systems generate content to replace removed objects, then object removal is achieved, but the generated content is non-photorealistic due to limited context
Solution Approach 1:
The system captures additional context information from the real-world scene using the camera's sensors and image stream before generating replacement content. By analyzing the live preview feed and surrounding environment during capture, the machine learning models access richer contextual data about lighting, textures, and scene geometry, enabling photorealistic content generation that matches the original scene conditions.
3Productivity
If post-capture editing is performed to remove objects, then object removal can be achieved, but computing resources are consumed after the image is already captured
Solution Approach 1:
The system performs all computationally intensive operations—object detection, segmentation, content generation, and composite image creation—during the capture phase when the camera is already actively processing the scene. This utilizes the existing computing resources of the capture device efficiently, avoiding additional post-capture processing and reducing overall energy consumption for the complete workflow.
4Ease of operation
If conventional editing systems are used, then object removal is possible, but the process lacks flexibility and requires multiple editing steps
Solution Approach 1:
The system merges multiple editing operations—object detection, object segmentation, content generation, and image compositing—into a single integrated workflow that occurs automatically during capture. The machine learning models and processing pipeline are combined into one cohesive system that handles the entire object removal process in real-time, simplifying the user experience while managing complexity internally through unified architecture.
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.