Hierarchical vectorization for facial images  

作　　者：Qian Fu Linlin Liu Fei Hou Ying He 

机构地区：[1]School of Computer Science and Engineering,Nanyang Technological University,639798,Singapore,Singapore [2]Data61,Commonwealth Scientific and Industrial Research Organisation,Sydney,2015,Australia [3]Interdisciplinary Graduate School,Nanyang Technological University and Alibaba Group,639798,Singapore,Singapore [4]State Key Laboratory of Computer Science,Institute of Software,Chinese Academy of Sciences,Beijing,100190,China [5]University of Chinese Academy of Sciences,Beijing,100049,China

出　　处：《Computational Visual Media》2024年第1期97-118,共22页计算可视媒体（英文版）

基　　金：This project was supported by the Ministry of Education,Singapore,under its Academic Research Fund Tier 1(RG20/20);the National Natural Science Foundation of China(61872347);the Special Plan for the Development of Distinguished Young Scientists of ISCAS(Y8RC535018).

摘　　要：The explosive growth of social media means portrait editing and retouching are in high demand.While portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly skilled.Aiming at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation.The base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression editing.The middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of PRs.The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation.We train a deep generative model that can produce high-frequency residuals automatically.Thanks to the inherent meaning in vector primitives,editing portraits becomes easy and intuitive.In particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic retouching.To quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider illumination.The new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait images.We evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.

关 键 词：face editing VECTORIZATION Poisson editing color transfer illumination editing expression editing 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]