Monocular 3D face reconstruction plays a crucial role in avatar generation,
with significant demand in web-related applications such as generating virtual
financial advisors in FinTech. Current reconstruction methods predominantly
rely on deep learning techniques and employ 2D self-supervision as a means to
guide model learning. However, these methods encounter challenges in capturing
the comprehensive 3D structural information of the face due to the utilization
of 2D images for model training purposes. To overcome this limitation and
enhance the reconstruction of 3D structural features, we propose an innovative
approach that integrates existing 2D features with 3D features to guide the
model learning process. Specifically, we introduce the 3D-ID Loss, which
leverages the high-dimensional structure features extracted from a
Spectral-Based Graph Convolution Encoder applied to the facial mesh. This
approach surpasses the sole reliance on the 3D information provided by the
facial mesh vertices coordinates. Our model is trained using 2D-3D data pairs
from a combination of datasets and achieves state-of-the-art performance on the
NoW benchmark.

通过将现有的 2D 特征与 3D 特征相结合，引入 3D-ID Loss 指导模型学习过程，提出一种创新方法来克服当前单眼 3D 人脸重建方法中所遇到的挑战，从而提高 3D 结构特征的重建效果。