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：模拟护肤品对面部的作用是一种潜在的新方法，可用于皮肤诊断和产品推荐中传达护肤品功效。此外，此类模拟使个人能预判自身皮肤状况，从而更好地管理皮肤健康。然而，目前缺乏有效的模拟手段。本文首次提出了揭示使用护肤品后毛孔变化的模拟模型。我们的模拟流程包含两个步骤：训练数据建立和面部毛孔模拟。为建立训练数据，我们从短期（8周）临床研究中收集了具有不同毛孔质量指数的面部图像。人们常因自然节律、外部压力等因素经历显著的皮肤波动，导致临床数据中出现较大扰动。为解决这一问题，我们提出了一种滑动窗口机制来清洗数据，并选择代表性指数来表征毛孔变化。面部毛孔模拟阶段包含三个模块：基于UNet的分割模块用于定位毛孔；回归模块用于预测随时间变化的变形超参数；变形模块则以变形超参数和毛孔分割标签为输入，精确地使毛孔相应变形。所提出的模拟能够渲染逼真的面部毛孔变化。这项研究将为面部皮肤模拟及护肤品开发的未来研究奠定基础。