Abstract: To resolve the escalating need for quickly generating high-precision digital humans in fields such as digital entertainment,virtual reality,and the metaverse,this paper proposes a novel method for rapidly generating a high-precision face model based on monocular RGB videos.Meanwhile,a new framework dedicated to precise modeling of the facial and neck regions is constructed.In particular,the proposed framework integrates neural primitives into a parameterized model of the head and neck,utilizing Head-And-neCK(Hereinafter referred to as HACK) as a superior alternative to the widely adopted Face Latent Animated Mesh Estimator(Hereinafter referred to as FLAME).This substitution markedly enhances the precision and efficiency of 3D facial reconstruction.Additionally,the proposed method has designed a real-time adaptive neural radiance field that significantly accelerates the training and reconstruction processes.By introducing a multi-resolution hash grid and employing the nearest triangle search for deformation gradient calculation within the deformation space,the proposed method achieves rapid reconstruction of high-fidelity head and neck models within minutes.Extensive quantitative and qualitative evaluations demonstrate that the proposed model exhibits notable improvements in both rendering quality and training time compared to existing state-of-the-art methods.

Key words: Head avatar,Neural radiance field,High-fidelity model,HACK model,Deep learning