A Deep Learning-based Virtual Oculoplastic Surgery Simulator

Seonghyeon Kim, Chang Wook Seo, Kwanggyoon Seo, Seung Han Song, Junyong Noh

Abstract: Oculoplastic surgery is a critical treatment for various eye conditions, such as ptosis, which can cause both aesthetic and functional issues. Due to the anxiety about the outcome, patients are often hesitant to undergo the necessary procedures required for the surgery. Virtual oculoplastic surgery simulation technology offers a solution to alleviate these concerns by providing realistic previews of post-surgical results. In this paper, we present a novel deep learning-based virtual oculoplastic surgery simulation system that addresses the limitations of existing methods. The proposed system aims to improve the accuracy of simulations by considering the anatomical structure and characteristics of the eye. Our method utilizes a deformable parametric mesh to enhance the controllability of the image transformation process. Furthermore, the combination of a style-based generator and a neural texture has been implemented to generate high-quality results. The proposed system is expected to facilitate better communication between doctors and patients by providing anatomically inspired high-quality simulation results. The development of this advanced virtual simulation system has the potential to enhance patient experiences and improve satisfaction with outcomes in the field of oculoplastic surgery.

External IDs:dblp:journals/tog/KimSSSN25