One crucial aspect of 3D head avatar reconstruction lies in the details of
facial expressions. Although recent NeRF-based photo-realistic 3D head avatar
methods achieve high-quality avatar rendering, they still encounter challenges
retaining intricate facial expression details because they overlook the
potential of specific expression variations at different spatial positions when
conditioning the radiance field. Motivated by this observation, we introduce a
novel Spatially-Varying Expression (SVE) conditioning. The SVE can be obtained
by a simple MLP-based generation network, encompassing both spatial positional
features and global expression information. Benefiting from rich and diverse
information of the SVE at different positions, the proposed SVE-conditioned
neural radiance field can deal with intricate facial expressions and achieve
realistic rendering and geometry details of high-fidelity 3D head avatars.
Additionally, to further elevate the geometric and rendering quality, we
introduce a new coarse-to-fine training strategy, including a geometry
initialization strategy at the coarse stage and an adaptive importance sampling
strategy at the fine stage. Extensive experiments indicate that our method
outperforms other state-of-the-art (SOTA) methods in rendering and geometry
quality on mobile phone-collected and public datasets.

通过引入一种新颖的空间可变表情（SVE）条件，结合具有空间位置特征和全局表情信息的简单的 MLP 生成网络，所提出的 SVE 条件神经辐射场可以处理复杂的面部表情并实现高保真度的 3D 头像的逼真渲染和几何细节，此外，通过引入新的由粗到精的训练策略，包括粗阶段的几何初始化策略和精细阶段的自适应重要性采样策略，我们的方法在手机收集和公共数据集上在渲染和几何质量方面优于其他最先进的方法。