In this paper, we introduce FitMe, a facial reflectance model and a
differentiable rendering optimization pipeline, that can be used to acquire
high-fidelity renderable human avatars from single or multiple images. The
model consists of a multi-modal style-based generator, that captures facial
appearance in terms of diffuse and specular reflectance, and a PCA-based shape
model. We employ a fast differentiable rendering process that can be used in an
optimization pipeline, while also achieving photorealistic facial shading. Our
optimization process accurately captures both the facial reflectance and shape
in high-detail, by exploiting the expressivity of the style-based latent
representation and of our shape model. FitMe achieves state-of-the-art
reflectance acquisition and identity preservation on single "in-the-wild"
facial images, while it produces impressive scan-like results, when given
multiple unconstrained facial images pertaining to the same identity. In
contrast with recent implicit avatar reconstructions, FitMe requires only one
minute and produces relightable mesh and texture-based avatars, that can be
used by end-user applications.

FitMe 是一种面部反射模型和可微分渲染优化管线，能够从单个或多个图像中获取高保真演示人体化身，具有多模态样式生成器，PCA 基于形状模型，快速可微分渲染过程，实现光真实的面部着色，实现反射获得和身份保存方面的最新成果。