3D head avatars built with neural implicit volumetric representations have
achieved unprecedented levels of photorealism. However, the computational cost
of these methods remains a significant barrier to their widespread adoption,
particularly in real-time applications such as virtual reality and
teleconferencing. While attempts have been made to develop fast neural
rendering approaches for static scenes, these methods cannot be simply employed
to support realistic facial expressions, such as in the case of a dynamic
facial performance. To address these challenges, we propose a novel fast 3D
neural implicit head avatar model that achieves real-time rendering while
maintaining fine-grained controllability and high rendering quality. Our key
idea lies in the introduction of local hash table blendshapes, which are
learned and attached to the vertices of an underlying face parametric model.
These per-vertex hash-tables are linearly merged with weights predicted via a
CNN, resulting in expression dependent embeddings. Our novel representation
enables efficient density and color predictions using a lightweight MLP, which
is further accelerated by a hierarchical nearest neighbor search method.
Extensive experiments show that our approach runs in real-time while achieving
comparable rendering quality to state-of-the-arts and decent results on
challenging expressions.

提出了一种新颖的快速 3D 神经隐式头像模型，实现实时渲染并保持细粒度的可控性和高渲染质量。本方法引入局部哈希表混合形状，通过线性合并和卷积神经网络预测的权重，将其附加到底层面部参数模型的顶点上，从而实现表情相关的嵌入。通过轻量级多层感知机实现高效的密度和颜色预测，并通过分层最近邻搜索方法加速。大量实验证明，本方法在实时运行的同时实现了与最新技术相比可比的渲染质量，并对具有挑战性的表情获得了不错的结果。