典型文献
Deep image synthesis from intuitive user input:A review and perspectives
文献摘要：
In many applications of computer graphics,art,and design,it is desirable for a user to provide intuitive non-image input,such as text,sketch,stroke,graph,or layout,and have a computer system automatically generate photo-realistic images according to that input.While classically,works that allow such automatic image content generation have followed a framework of image retrieval and composition,recent advances in deep generative models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and flow-based methods have enabled more powerful and versatile image generation approaches.This paper reviews recent works for image synthesis given intuitive user input,covering advances in input versatility,image generation methodology,benchmark datasets,and evaluation metrics.This motivates new perspectives on input representation and interactivity,cross fertilization between major image generation paradigms,and evaluation and comparison of generation methods.
文献关键词：
中图分类号：
作者姓名：
Yuan Xue;Yuan-Chen Guo;Han Zhang;Tao Xu;Song-Hai Zhang;Xiaolei Huang
作者机构：
College of Information Sciences and Technology,the Pennsylvania State University,University Park,PA,USA;Department of Computer Science and Technology,Tsinghua University,Beijing,China,and Beijing National Research Center for Information Science and Technology(BNRist),Tsinghua University,Beijing,China;Google Brain,Mountain View,CA,USA;Facebook,Menlo Park,CA,USA
文献出处：
引用格式：
[1]Yuan Xue;Yuan-Chen Guo;Han Zhang;Tao Xu;Song-Hai Zhang;Xiaolei Huang-.Deep image synthesis from intuitive user input:A review and perspectives)[J].计算可视媒体（英文）,2022(01):3-31
A类：
VAEs
B类：
Deep,synthesis,from,intuitive,user,input,perspectives,In,many,applications,computer,graphics,art,design,desirable,provide,such,text,sketch,stroke,layout,have,system,automatically,generate,photo,realistic,images,according,that,While,classically,allow,content,generation,followed,framework,retrieval,composition,recent,advances,deep,generative,models,adversarial,networks,GANs,variational,autoencoders,flow,methods,enabled,more,powerful,versatile,approaches,This,paper,reviews,given,covering,versatility,methodology,benchmark,datasets,evaluation,metrics,motivates,new,representation,interactivity,cross,fertilization,between,major,paradigms,comparison
AB值：
0.604088
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