3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes
This addresses facial scar regeneration for patients, though it appears incremental as it builds on existing mesh autoencoder and graph processing methods.
This study tackled the problem of regenerating 3D facial imperfections like scars using a deep learning approach called 3D-FaIR, which recreates filling parts for facial scars based on remaining facial features and includes an improved outlier technique for wound separation.
This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning approaches in graph processing and analysis to investigate the capabilities model in recreating a filling part for facial scars. Our approach in dataset creation is able to generate a facial scar rationally in a virtual space that corresponds to the unique circumstances. Especially, we propose a new method which is named 3D Facial Imperfection Regeneration(3D-FaIR) for reproducing a complete face reconstruction based on the remaining features of the patient face. To further enhance the applicable capacity of the present research, we develop an improved outlier technique to separate the wounds of patients and provide appropriate wound cover models. Also, a Cir3D-FaIR dataset of imperfect faces and open codes was released at https://github.com/SIMOGroup/3DFaIR. Our findings demonstrate the potential of the proposed approach to help patients recover more quickly and safely through convenient techniques. We hope that this research can contribute to the development of new products and innovative solutions for facial scar regeneration.