CodeFormer is an advanced face restoration algorithm designed to enhance and reconstruct facial images, particularly those that are degraded or AI-generated. Developed by researchers at Nanyang Technological University, CodeFormer employs a Transformer-based architecture to predict and restore facial features, effectively addressing issues like blurriness, low resolution, and artifacts.
Key Features:
- Transformer-Based Architecture: Utilizes a Transformer model to capture global facial compositions, enabling accurate restoration of facial details.
- Discrete Codebook Learning: Learns a discrete codebook to represent high-quality visual components of faces, aiding in the reconstruction process.
- Controllable Feature Transformation: Incorporates a module that allows adjustment between fidelity and quality, providing flexibility in restoration outcomes.
Applications/Use Cases:
- Restoring Old Photographs: Enhances and revives historical photos, improving clarity and detail.
- Improving AI-Generated Faces: Refines faces produced by AI models, reducing artifacts and enhancing realism.
- Medical Imaging: Assists in reconstructing facial images from medical scans, aiding in diagnostics and treatment planning.