  Recently, the reconstruction of high-fidelity 3D head models from static
portrait image has made great progress. However, most methods require
multi-view or multi-illumination information, which therefore put forward high
requirements for data acquisition. In this paper, we study the reconstruction
of high-fidelity 3D head models from arbitrary monocular videos. Non-rigid
structure from motion (NRSFM) methods have been widely used to solve such
problems according to the two-dimensional correspondence between different
frames. However, the inaccurate correspondence caused by high-complex hair
structures and various facial expression changes would heavily influence the
reconstruction accuracy. To tackle these problems, we propose a prior-guided
dynamic implicit neural network. Specifically, we design a two-part dynamic
deformation field to transform the current frame space to the canonical one. We
further model the head geometry in the canonical space with a learnable signed
distance field (SDF) and optimize it using the volumetric rendering with the
guidance of two-main head priors to improve the reconstruction accuracy and
robustness. Extensive ablation studies and comparisons with state-of-the-art
methods demonstrate the effectiveness and robustness of our proposed method.
