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Diffusion-Based Facial Aesthetics Enhancement With 3D Structure Guidance.
Summary
This study introduces Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion) for facial aesthetics enhancement (FAE). The method improves attractiveness while preserving facial identity, outperforming existing techniques.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Processing
Background:
- Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness while preserving identity.
- Existing FAE methods using deep features or scores may cause excessive beautification or identity loss.
Purpose of the Study:
- To develop a novel FAE method that enhances facial attractiveness with minimal identity loss.
- To introduce a diffusion-based approach leveraging 3D structure guidance.
Main Methods:
- Proposed Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion).
- Extracted FAE guidance from a nearest neighbor reference face.
- Recovered a 3D face model using input and reference faces for depth and contour guidance.
- Utilized Stable Diffusion with ControlNet for FAE.
Main Results:
- NNSG-Diffusion effectively beautifies 2D facial images.
- The method demonstrates superior performance in enhancing facial aesthetics.
- Facial identity preservation is significantly improved compared to prior methods.
Conclusions:
- NNSG-Diffusion offers a robust solution for FAE with enhanced identity consistency.
- The 3D structure guidance approach effectively balances beautification and identity preservation.
- This method advances the state-of-the-art in facial image manipulation.
