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的影响，同时也受到静态细节（例如，个体特定外观）和动态细节（例如，表情驱动的皱纹）的影响。以往的研究难以通过图像级监督来解耦静态与动态细节，导致重建结果不够真实。本文旨在实现高保真三维人脸重建，提出HiFace方法显式建模静态与动态细节。具体而言，静态细节通过位移基的线性组合进行建模，而动态细节则通过两种极端表情下的位移图线性插值进行建模。我们利用多种损失函数，在合成与真实数据集上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在重建质量上达到当前最优水平，并忠实恢复了静态与动态细节。我们的项目页面见https://project-hiface.github.io。