  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.
