Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics applications.Though significant progress has been made in recent years,how to effectively and efficien...Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics applications.Though significant progress has been made in recent years,how to effectively and efficiently explore normal priors remains challenging.Most existing approaches depend on the development of intricate network architectures and complex calculations 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 predicted normals with marginal model parameters and computational cost.Meanwhile,it can mitigate the imbalance issue between training data and real-world face images due to the progressive refinement mechanism,and thus boost the generalization 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 addition,our shared-parameter cascaded block employs a recurrent mechanism,allowing it to be applied multiple times for optimization 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-artmethods.The code is available at https://github.com/AutoHDR/CasFNE.git.展开更多
基金supported by the National Natural Science Foundation of China(62072327)。
文摘Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics applications.Though significant progress has been made in recent years,how to effectively and efficiently explore normal priors remains challenging.Most existing approaches depend on the development of intricate network architectures and complex calculations 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 predicted normals with marginal model parameters and computational cost.Meanwhile,it can mitigate the imbalance issue between training data and real-world face images due to the progressive refinement mechanism,and thus boost the generalization 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 addition,our shared-parameter cascaded block employs a recurrent mechanism,allowing it to be applied multiple times for optimization 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-artmethods.The code is available at https://github.com/AutoHDR/CasFNE.git.
