Accurate representations of 3D faces are of paramount importance in various
computer vision and graphics applications. However, the challenges persist due
to the limitations imposed by data discretization and model linearity, which
hinder the precise capture of identity and expression clues in current studies.
This paper presents a novel 3D morphable face model, named ImFace++, to learn a
sophisticated and continuous space with implicit neural representations.
ImFace++ first constructs two explicitly disentangled deformation fields to
model complex shapes associated with identities and expressions, respectively,
which simultaneously facilitate the automatic learning of correspondences
across diverse facial shapes. To capture more sophisticated facial details, a
refinement displacement field within the template space is further
incorporated, enabling a fine-grained learning of individual-specific facial
details. Furthermore, a Neural Blend-Field is designed to reinforce the
representation capabilities through adaptive blending of an array of local
fields. In addition to ImFace++, we have devised an improved learning strategy
to extend expression embeddings, allowing for a broader range of expression
variations. Comprehensive qualitative and quantitative evaluations demonstrate
that ImFace++ significantly advances the state-of-the-art in terms of both face
reconstruction fidelity and correspondence accuracy.

本研究提出了一种名为 ImFace++ 的新型 3D 可塑性面部模型，通过使用隐式神经表示来学习一种精细连续空间，并通过构建两个明确定义的变形场和一个优化的神经融合场来模拟面部的复杂形状、个体身份和表情变化，进而在面部重建和对应精度方面显著提升了现有技术水平。