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<p>This study introduces an effective approach using a fully convolutional mesh autoencoder model to reconstruct 3D facial features in the presence of imperfections. The method accurately simulates facial scars in a virtual environment, adapting to unique situations. This article presents the “Cir3D-FaIR” dataset, which is specifically tailored to address issues related to facial scars. Additionally, we propose a new technique called 3D facial imperfection regeneration (3D-FaIR), which focusses on reconstructing a complete face based on the remaining features of the patient’s face. To further enhance the applicability of this research, the article has developed an advanced outlier detection technique that isolates affected areas and provides appropriate models for wound coverage. The Cir3D-FaIR dataset, consisting of imperfect facial models and open-source package, is available at <a href="https://github.com/SIMOGroup/3DFaIR">https://github.com/SIMOGroup/3DFaIR</a>. Our findings demonstrate that the proposed approach can potentially aid in faster and safer patient recovery through convenient methods. We hope that this work inspires the development of new products and innovative solutions for facial scar regeneration.</p>

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3D-FaIR: 3D facial imperfection regeneration with defects by fully convolutional mesh autoencoder

  • 期刊论文

  • 发表日期：

    2025年3月8日

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  • Phuong D. Nguyen,
  • Thinh D. Le,
  • Duong Q. Nguyen,
  • Thanh Q. Nguyen,
  • Li-Wei Chou,
  • H. Nguyen-Xuan

摘要

This study introduces an effective approach using a fully convolutional mesh autoencoder model to reconstruct 3D facial features in the presence of imperfections. The method accurately simulates facial scars in a virtual environment, adapting to unique situations. This article presents the “Cir3D-FaIR” dataset, which is specifically tailored to address issues related to facial scars. Additionally, we propose a new technique called 3D facial imperfection regeneration (3D-FaIR), which focusses on reconstructing a complete face based on the remaining features of the patient’s face. To further enhance the applicability of this research, the article has developed an advanced outlier detection technique that isolates affected areas and provides appropriate models for wound coverage. The Cir3D-FaIR dataset, consisting of imperfect facial models and open-source package, is available at https://github.com/SIMOGroup/3DFaIR. Our findings demonstrate that the proposed approach can potentially aid in faster and safer patient recovery through convenient methods. We hope that this work inspires the development of new products and innovative solutions for facial scar regeneration.