Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graph-ics applications.Though significant progress has been made in recent years,how to effectively and efficiently explore normal pri-ors remains challenging.Most existing approaches depend on the development of intricate network architectures and complex cal-culations for in-the-wild face images.To overcome the above issue,we propose a simple yet effective cascaded neural network,called Cas-FNE,which progressively boosts the quality of pre-dicted normals with marginal model parameters and computa-tional cost.Meanwhile,it can mitigate the imbalance issue between training data and real-world face images due to the pro-gressive refinement mechanism,and thus boost the generaliza-tion ability of the model.Specifically,in the training phase,our model relies solely on a small amount of labeled data.The earlier prediction serves as guidance for following refinement.In addi-tion,our shared-parameter cascaded block employs a recurrent mechanism,allowing it to be applied multiple times for optimiza-tion without increasing network parameters.Quantitative and qualitative evaluations on benchmark datasets are conducted to show that our Cas-FNE can faithfully maintain facial details and reveal its superiority over state-of-the-art methods.The code is available at https://github.com/AutoHDR/CasFNE.git.
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