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The problem of modeling an animatable 3D human head avatar under light-weightsetups is of significant importance but has not been well solved. Existing 3Drepresentations either perform well in the realism of portrait images synthesisor the accuracy of expression control, but not both. To address the problem, weintroduce a novel hybrid explicit-implicit 3D representation, Facial ModelConditioned Neural Radiance Field, which integrates the expressiveness of NeRFand the prior information from the parametric template. At the core of ourrepresentation, a synthetic-renderings-based condition method is proposed tofuse the prior information from the parametric model into the implicit fieldwithout constraining its topological flexibility. Besides, based on the hybridrepresentation, we properly overcome the inconsistent shape issue presented inexisting methods and improve the animation stability. Moreover, by adopting anoverall GAN-based architecture using an image-to-image translation network, weachieve high-resolution, realistic and view-consistent synthesis of dynamichead appearance. Experiments demonstrate that our method can achievestate-of-the-art performance for 3D head avatar animation compared withprevious methods.

HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field