Based on a combined data set of 4000 high resolution facial scans, we
introduce a non-linear morphable face model, capable of producing multifarious
face geometry of pore-level resolution, coupled with material attributes for
use in physically-based rendering. We aim to maximize the variety of face
identities, while increasing the robustness of correspondence between unique
components, including middle-frequency geometry, albedo maps, specular
intensity maps and high-frequency displacement details. Our deep learning based
generative model learns to correlate albedo and geometry, which ensures the
anatomical correctness of the generated assets. We demonstrate potential use of
our generative model for novel identity generation, model fitting,
interpolation, animation, high fidelity data visualization, and low-to-high
resolution data domain transferring. We hope the release of this generative
model will encourage further cooperation between all graphics, vision, and data
focused professionals while demonstrating the cumulative value of every
individual's complete biometric profile.

本研究提出了一种基于深度学习的生成模型，结合 4000 高分辨率人脸扫描数据集，能够产生多种面部几何、材料属性的人脸模型，并可用于物理渲染，旨在增加面部身份的多样性，提高唯一组件之间的韧性，包括中频几何、反照率图、高频位移细节等，可用于新颖的身份生成、模型拟合、插值、动画、高保真数据可视化和低到高分辨率数据域转换，并有望鼓励所有图形、视觉和数据专业人士之间进一步的合作，展示每个人完整生物特征资料的累积价值。