DeepFaceReshaping:Interactive deep face reshaping via landmark manipulation  

作　　者：Shu-Yu Chen Yue-Ren Jiang Hongbo Fu Xinyang Han Zitao Liu Rong Li Lin Gao 

机构地区：[1]Beijing Key Laboratory of Mobile Computing and Pervasive Device,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]School of Creative Media,City University of Hong Kong,Hong Kong,China [4]TAL Education Group,Beijing 100081,China [5]Guangdong Institute of Smart Education,Jinan University,Guangzhou 510632,China [6]Zhejiang Lab Nanhu Headquarters,Hangzhou 310023

出　　处：《Computational Visual Media》2024年第5期949-963,共15页计算可视媒体（英文版）

基　　金：supported by grants from the Open Researchh Projects of Zhejiang Lab(No.2021KE0AB06);the National Natural Science Foundation of China(Nos.62061136007 and 62102403);the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars(No.JQ21013);d the Open Project Program of the State Key Laboratory of Virtual Reality Technology and Systems,Beihang University(No.VRLAB2022C07).

摘　　要：Deep generative models allow the synthesis of realistic human faces from freehand sketches or semantic maps.However,although they are flexible,sketches and semantic maps provide too much freedom for manipulation,and thus,are not easy for novice users to control.In this study,we present DeepFaceReshaping,a novel landmarkbased deep generative framework for interactive face reshaping.To edit the shape of a face realistically by manipulating a small number of face landmarks,we employ neural shape deformation to reshape individual face components.Furthermore,we propose a novel Transformer-based partial refinement network to synthesize the reshaped face components conditioned on the edited landmarks,and fuse the components to generate the entire face using a local-to-global approach.In this manner,we limit possible reshaping effects within a feasible component-based face space.Thus,our interface is intuitive even for novice users,asconfirmed by auser study.Our experiments demonstrate that our method outperforms traditional warping-based approaches and recent deep generative techniques.

关 键 词：face reshaping deep generative model interactive editing 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]