Open Access

ARTICLE

From Imperfection to Perfection: Advanced 3D Facial Reconstruction Using MICA Models and Self-Supervision Learning

Thinh D. Le, Duong Q. Nguyen, Phuong D. Nguyen, H. Nguyen-Xuan^*

CIRTECH Institute, HUTECH University, Ho Chi Minh City, 72308, Viet Nam

* Corresponding Author: H. Nguyen-Xuan. Email:

Computer Modeling in Engineering & Sciences 2025, 142(2), 1459-1479. https://doi.org/10.32604/cmes.2024.056753

Received 30 July 2024; Accepted 03 September 2024; Issue published 27 January 2025

Abstract

Research on reconstructing imperfect faces is a challenging task. In this study, we explore a data-driven approach using a pre-trained MICA (MetrIC fAce) model combined with 3D printing to address this challenge. We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction. Our results demonstrate high accuracy, evaluated by the geometric loss function and various statistical measures. To showcase the effectiveness of the approach, we used 3D printing to create a model that covers facial wounds. The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction. This technique has the potential to aid doctors in treating patients with facial injuries.

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Keywords

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