  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.
