Simulating the effects of skincare products on face is a potential new way to communicate the efficacy of skincare products in skin diagnostics and product recommendations. Furthermore, such simulations enable one to anticipate his/her skin conditions and better manage skin health. However, there is a lack of effective simulations today. In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products. Our simulation pipeline consists of 2 steps: training data establishment and facial pore simulation. To establish training data, we collect face images with various pore quality indexes from short-term (8-weeks) clinical studies. People often experience significant skin fluctuations (due to natural rhythms, external stressors, etc.,), which introduces large perturbations in clinical data. To address this problem, we propose a sliding window mechanism to clean data and select representative index(es) to represent facial pore changes. Facial pore simulation stage consists of 3 modules: UNet-based segmentation module to localize facial pores; regression module to predict time-dependent warping hyperparameters; and deformation module, taking warping hyperparameters and pore segmentation labels as inputs, to precisely deform pores accordingly. The proposed simulation is able to render realistic facial pore changes. And this work will pave the way for future research in facial skin simulation and skincare product developments.
翻译:模拟皮肤护理产品对面部的影响是一种潜在的新方式,通过皮肤诊断和产品建议来交流皮肤护理产品在皮肤诊断和产品建议中的功效。此外,这种模拟使一个人能够预测皮肤状况并更好地管理皮肤健康。然而,今天缺乏有效的模拟。在本文中,我们提出第一个模拟模型,以在使用皮肤护理产品后显示面孔变化。我们的模拟管道包括两个步骤:培训数据建立和面孔模拟。为了建立培训数据,我们从短期(8周)临床研究中收集面部图像,并用各种孔质量指数来收集。人们经常经历严重的皮肤波动(由于自然节律、外部压力调节器等),从而在临床数据中引入大规模扰动。为了解决这一问题,我们建议采用一个滑动窗口机制来清理数据,并选择代表面孔变化的代表性指数。 面孔模拟阶段由3个模块组成:基于UNet的面部分解模块,以将面部肿瘤地方化;回归模块,以预测依赖时间的超焦度计;以及脱形模块,在进行超常调时,进行超常动的皮肤分析时,从而在模拟过程中进行超常变形分析,进行超常进行超常的皮肤分析,从而进行模拟研究,从而进行精确地将模拟成模模模模模模模模模模模模模模模模模模模模模模模模模模制成。