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.

Abstract Facial sketches are both a concise way of showing the identity of a person and a means to express artistic intention. While a few techniques have recently emerged that allow sketches to be extracted in different styles, they typically rely on a large amount of data that is difficult to obtain. Here, we propose StyleSketch, a method for extracting high‐resolution stylized sketches from a face image. Using the rich semantics of the deep features from a pretrained StyleGAN, we are able to train a sketch generator with 16 pairs of face and the corresponding sketch images. The sketch generator utilizes part‐based losses with two‐stage learning for fast convergence during training for high‐quality sketch extraction. Through a set of comparisons, we show that StyleSketch outperforms existing state‐of‐the‐art sketch extraction methods and few‐shot image adaptation methods for the task of extracting high‐resolution abstract face sketches. We further demonstrate the versatility of StyleSketch by extending its use to other domains and explore the possibility of semantic editing. The project page can be found in https://kwanyun.github.io/stylesketch_project .

Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.

We propose a model that extracts a sketch from a colorized image in such a way that the extracted sketch has a line style similar to a given reference sketch while preserving the visual content identically to the colorized image. Authentic sketches drawn by artists have various sketch styles to add visual interest and contribute feeling to the sketch. However, existing sketch-extraction methods generate sketches with only one style. Moreover, existing style transfer models fail to transfer sketch styles because they are mostly designed to transfer textures of a source style image instead of transferring the sparse line styles from a reference sketch. Lacking the necessary volumes of data for standard training of translation systems, at the core of our GAN-based solution is a self-reference sketch style generator that produces various reference sketches with a similar style but different spatial layouts. We use independent attention modules to detect the edges of a colorized image and reference sketch as well as the visual correspondences between them. We apply several loss terms to imitate the style and enforce sparsity in the extracted sketches. Our sketch-extraction method results in a close imitation of a reference sketch style drawn by an artist and outperforms all baseline methods. Using our method, we produce a synthetic dataset representing various sketch styles and improve the performance of auto-colorization models, in high demand in comics. The validity of our approach is confirmed via qualitative and quantitative evaluations.