Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the sim...Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.展开更多
基于组件的卡通人脸生成分为构件的组合及特征调整两阶段完成,可分别视为组合优化和连续优化问题解决。然而,人脸特征参数优化过程中很难用函数显性表示其优化目标,是典型的隐性目标优化问题。针对此问题,提出基于反向学习策略的交互式...基于组件的卡通人脸生成分为构件的组合及特征调整两阶段完成,可分别视为组合优化和连续优化问题解决。然而,人脸特征参数优化过程中很难用函数显性表示其优化目标,是典型的隐性目标优化问题。针对此问题,提出基于反向学习策略的交互式差分演化算法(interactive differential evolution algorithm based on opposition-based learning strategy,IDE-OBL),将传统交互式演化算法中人为提供适应值的交互方式转化为成对比较的方式,采用反向学习策略加快算法收敛,在一定程度上减少了用户评价次数。实验结果表明,在基于组件的卡通人脸生成问题中,IDE-OBL比未使用反向学习策略的IGA和IDE要好,减少了演化迭代次数,有利于用户疲劳程度的缓解。展开更多
文摘Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.
文摘基于组件的卡通人脸生成分为构件的组合及特征调整两阶段完成,可分别视为组合优化和连续优化问题解决。然而,人脸特征参数优化过程中很难用函数显性表示其优化目标,是典型的隐性目标优化问题。针对此问题,提出基于反向学习策略的交互式差分演化算法(interactive differential evolution algorithm based on opposition-based learning strategy,IDE-OBL),将传统交互式演化算法中人为提供适应值的交互方式转化为成对比较的方式,采用反向学习策略加快算法收敛,在一定程度上减少了用户评价次数。实验结果表明,在基于组件的卡通人脸生成问题中,IDE-OBL比未使用反向学习策略的IGA和IDE要好,减少了演化迭代次数,有利于用户疲劳程度的缓解。
