CASIA OpenIR  > 智能感知与计算研究中心
Age progression and regression with spatial attention modules
Li, Qi; Liu, Yunfan; Sun, Zhenan
Conference NameAAAI conference on Artificial Intelligence
Conference Date2020
Conference PlaceUSA

Age progression and regression refers to aesthetically rendering a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping ageirrelevant regions unchanged.

IS Representative Paper
Sub direction classification生物特征识别
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type会议论文
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Qi,Liu, Yunfan,Sun, Zhenan. Age progression and regression with spatial attention modules[C],2020.
Files in This Item: Download All
File Name/Size DocType Version Access License
6800-Article Text-10(3767KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Qi]'s Articles
[Liu, Yunfan]'s Articles
[Sun, Zhenan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Qi]'s Articles
[Liu, Yunfan]'s Articles
[Sun, Zhenan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Qi]'s Articles
[Liu, Yunfan]'s Articles
[Sun, Zhenan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 6800-Article Text-10029-1-10-20200524 (10).pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.