Abstract
Automatically manipulating facial attributes is challenging because it needs to modify the facial appearances, while keeping not only the person's identity but also the realism of the resultant images. Unlike the prior works on the facial attribute parsing, we aim at an inverse and more challenging problem called attribute manipulation by modifying a facial image in line with a reference facial attribute. Given a source input image and reference images with a target attribute, our goal is to generate a new image (i.e., target image) that not only possesses the new attribute but also keeps the same or similar content with the source image. In order to generate new facial attributes, we train a deep neural network with a combination of a perceptual content loss and two adversarial losses, which ensure the global consistency of the visual content while implementing the desired attributes often impacting on local pixels. The model automatically adjusts the visual attributes on facial appearances and keeps the edited images as realistic as possible. The evaluation shows that the proposed model can provide a unified solution to both local and global facial attribute manipulation such as expression change and hair style transfer. Moreover, we further demonstrate that the learned attribute discriminator can be used for attribute localization.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 112-121 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781538648865 |
| DOIs | |
| State | Published - May 3 2018 |
| Event | 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States Duration: Mar 12 2018 → Mar 15 2018 |
Publication series
| Name | Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 |
|---|---|
| Volume | 2018-January |
Conference
| Conference | 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 |
|---|---|
| Country | United States |
| City | Lake Tahoe |
| Period | 3/12/18 → 3/15/18 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computer Science Applications
Cite this
}
Weakly supervised facial attribute manipulation via deep adversarial network. / Wang, Yilin; Wang, Suhang; Qi, Guojun; Tang, Jiliang; Li, Baoxin.
Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 112-121 (Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018; Vol. 2018-January).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Weakly supervised facial attribute manipulation via deep adversarial network
AU - Wang, Yilin
AU - Wang, Suhang
AU - Qi, Guojun
AU - Tang, Jiliang
AU - Li, Baoxin
PY - 2018/5/3
Y1 - 2018/5/3
N2 - Automatically manipulating facial attributes is challenging because it needs to modify the facial appearances, while keeping not only the person's identity but also the realism of the resultant images. Unlike the prior works on the facial attribute parsing, we aim at an inverse and more challenging problem called attribute manipulation by modifying a facial image in line with a reference facial attribute. Given a source input image and reference images with a target attribute, our goal is to generate a new image (i.e., target image) that not only possesses the new attribute but also keeps the same or similar content with the source image. In order to generate new facial attributes, we train a deep neural network with a combination of a perceptual content loss and two adversarial losses, which ensure the global consistency of the visual content while implementing the desired attributes often impacting on local pixels. The model automatically adjusts the visual attributes on facial appearances and keeps the edited images as realistic as possible. The evaluation shows that the proposed model can provide a unified solution to both local and global facial attribute manipulation such as expression change and hair style transfer. Moreover, we further demonstrate that the learned attribute discriminator can be used for attribute localization.
AB - Automatically manipulating facial attributes is challenging because it needs to modify the facial appearances, while keeping not only the person's identity but also the realism of the resultant images. Unlike the prior works on the facial attribute parsing, we aim at an inverse and more challenging problem called attribute manipulation by modifying a facial image in line with a reference facial attribute. Given a source input image and reference images with a target attribute, our goal is to generate a new image (i.e., target image) that not only possesses the new attribute but also keeps the same or similar content with the source image. In order to generate new facial attributes, we train a deep neural network with a combination of a perceptual content loss and two adversarial losses, which ensure the global consistency of the visual content while implementing the desired attributes often impacting on local pixels. The model automatically adjusts the visual attributes on facial appearances and keeps the edited images as realistic as possible. The evaluation shows that the proposed model can provide a unified solution to both local and global facial attribute manipulation such as expression change and hair style transfer. Moreover, we further demonstrate that the learned attribute discriminator can be used for attribute localization.
UR - http://www.scopus.com/inward/record.url?scp=85050983870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050983870&partnerID=8YFLogxK
U2 - 10.1109/WACV.2018.00019
DO - 10.1109/WACV.2018.00019
M3 - Conference contribution
AN - SCOPUS:85050983870
T3 - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
SP - 112
EP - 121
BT - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -