3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image. The facial surface is influenced by the coarse shape, as well as the static detail (e,g., person-specific appearance) and dynamic detail (e.g., expression-driven wrinkles). Previous work struggles to decouple the static and dynamic details through image-level supervision, leading to reconstructions that are not realistic. In this paper, we aim at high-fidelity 3D face reconstruction and propose HiFace to explicitly model the static and dynamic details. Specifically, the static detail is modeled as the linear combination of a displacement basis, while the dynamic detail is modeled as the linear interpolation of two displacement maps with polarized expressions. We exploit several loss functions to jointly learn the coarse shape and fine details with both synthetic and real-world datasets, which enable HiFace to reconstruct high-fidelity 3D shapes with animatable details. Extensive quantitative and qualitative experiments demonstrate that HiFace presents state-of-the-art reconstruction quality and faithfully recovers both the static and dynamic details. Our project page can be found at https://project-hiface.github.io

翻译：三维形变模型（3DMMs）在从单张图像重建逼真且可动画化的三维人脸表面方面展现出巨大潜力。人脸表面受粗粒度形状、静态细节（如个体特异性外观）以及动态细节（如表情驱动的皱纹）的共同影响。先前的工作难以通过图像级监督解耦静态与动态细节，导致重建结果不够真实。本文致力于高保真三维人脸重建，提出HiFace以显式建模静态与动态细节。具体而言，将静态细节建模为位移基向量的线性组合，动态细节则通过两个具有极化表情的位移图的线性插值进行建模。我们利用多种损失函数，结合合成数据集与真实世界数据集联合学习粗粒度形状与精细细节，使HiFace能够重建具有可动画细节的高保真三维形状。大量定量与定性实验表明，HiFace展现出最先进的重建质量，并忠实地恢复了静态与动态细节。我们的项目页面可访问 https://project-hiface.github.io。