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Updated: May 5, 2026

Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

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Published on: May 23, 2020

Diffusion-Based Facial Aesthetics Enhancement With 3D Structure Guidance.

Lisha Li, Jingwen Hou, Weide Liu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 21, 2025

Summary

This summary is machine-generated.

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.

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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.