3D facial avatar reconstruction has been a significant research topic in
computer graphics and computer vision, where photo-realistic rendering and
flexible controls over poses and expressions are necessary for many related
applications. Recently, its performance has been greatly improved with the
development of neural radiance fields (NeRF). However, most existing NeRF-based
facial avatars focus on subject-specific reconstruction and reenactment,
requiring multi-shot images containing different views of the specific subject
for training, and the learned model cannot generalize to new identities,
limiting its further applications. In this work, we propose a one-shot 3D
facial avatar reconstruction framework that only requires a single source image
to reconstruct a high-fidelity 3D facial avatar. For the challenges of lacking
generalization ability and missing multi-view information, we leverage the
generative prior of 3D GAN and develop an efficient encoder-decoder network to
reconstruct the canonical neural volume of the source image, and further
propose a compensation network to complement facial details. To enable
fine-grained control over facial dynamics, we propose a deformation field to
warp the canonical volume into driven expressions. Through extensive
experimental comparisons, we achieve superior synthesis results compared to
several state-of-the-art methods.

提出了一种只需要一张源图像就能重建高保真度的三维面部角色建模方法，通过使用 3D GAN 的生成先验和有效的编码器 - 解码器网络来重建源图像的规范神经体积，同时使用补偿网络来补充面部细节，并引入变形场来对面部表情进行精细控制。与多种先进方法相比，实验结果显示合成效果更加优越。