Targeted Facial Artifact Repair in Computer-Generated Images
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Solution Overview
Problem
Diffusion-based image generation models often produce images with artifacts such as asymmetries, distorted facial features, and unnatural holes, leading to unrealistic results, limiting the value and quality of the generated art.
Innovation Solution
An image processing apparatus that combines super-resolution techniques, inpainting, and targeted fixes like eye-opening and eye-closing using a modified co-modulated generative adversarial network (co-mod-GAN) architecture to correct facial irregularities and generate high-quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If diffusion-based image generation models are used to generate images, then image generation capability is improved, but artifacts and distorted facial features appear in the generated images
Solution Approach 1:
The patent segments the face into multiple regions (eyes, mouth, forehead, cheeks, chin) and applies targeted corrections to each region independently. The system identifies specific artifact-prone areas and processes them separately through specialized neural networks, allowing precise correction of facial features while preserving overall image quality.
Solution Approach 2:
The patent applies different processing techniques to different regions of the face based on their specific needs. For example, eye-closing/eye-opening operations are applied specifically to eye regions, while inpainting is applied to regions with holes or distortions. This localized approach ensures that each facial feature receives appropriate correction without affecting other regions.
2Manufacturing precision
If multiple correction operations are applied to remove artifacts, then image quality is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary operations such as eye-closing and eye-opening before final image generation. By pre-processing the image to correct known artifact-prone regions (eyes and mouth) before completing the diffusion process, the system reduces the need for extensive post-processing and accelerates overall correction time.
Solution Approach 2:
The patent applies correction operations selectively only to regions where artifacts are detected, rather than processing the entire image uniformly. This partial action approach focuses computational resources on problematic areas, reducing overall processing time while maintaining image quality.
Data Source
AI summary
Systems and methods for image processing are provided. Embodiments include identifying an image of a face that includes an artifact in a part of the face. A machine learning model generates an intermediate image based on the original image. The intermediate image depicts the part of the face in a closed position. Then the model generates a corrected image based on the intermediate image. The corrected image depicts the face with the part of the face in an open position and without the artifact.