Iterative Inpainting Using Artifact Segmentation Feedback
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Solution Overview
Problem
Conventional image generation systems lack accuracy and flexibility in digital image inpainting, often introducing perceptual artifacts such as broken structures and color bleeding, and require manual corrections, limiting their use to specific editing cases.
Innovation Solution
An artifact segmentation system using a machine-learning model trained on user-labeled perceptual artifacts to detect and iteratively reduce perceptual artifacts in digital images, employing an artifact segmentation machine-learning model to improve accuracy and flexibility in digital image editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image generation systems are used for digital image inpainting, then the basic inpainting function is achieved, but perceptual artifacts such as broken structures and color bleeding are introduced, reducing accuracy
Solution Approach 1:
The system implements feedback by training an artifact segmentation model on labeled artifact regions from synthetic training images. This model then detects perceptual artifacts in generated images and provides feedback to guide iterative inpainting operations, continuously improving accuracy by reducing artifacts in subsequent iterations
Solution Approach 2:
The system performs preliminary action by pre-training the artifact segmentation model on labeled artifact regions before actual inpainting operations. This preparation enables the system to quickly identify and correct artifacts during iterative inpainting, improving overall manufacturing precision
2Productivity
If conventional image generation systems are used for digital image inpainting, then the process can be completed, but manual corrections are required, reducing productivity
Solution Approach 1:
The system implements self-service through automated artifact detection and iterative inpainting. The artifact segmentation model automatically identifies perceptual artifacts, and the iterative inpainting process automatically corrects them without requiring manual intervention, significantly improving productivity while maintaining ease of operation
Solution Approach 2:
The feedback mechanism enables the system to automatically detect artifacts and guide corrective inpainting operations, eliminating the need for manual corrections and thereby increasing productivity
3Adaptability or versatility
If conventional image generation systems are used for digital image inpainting, then basic editing is possible, but flexibility is limited to specific editing cases
Solution Approach 1:
The system implements dynamics through iterative inpainting operations. The process dynamically adapts by performing multiple inpainting iterations, with each iteration detecting and reducing artifacts from the previous iteration. This dynamic approach enhances flexibility across different editing cases while reliably producing artifact-free results
Solution Approach 2:
The feedback loop enables the system to adapt to different editing scenarios by continuously detecting artifacts and adjusting inpainting operations, thereby improving both flexibility and reliability
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.