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- Publisher Website: 10.1109/CVPR42600.2020.00559
- Scopus: eid_2-s2.0-85093100763
- WOS: WOS:000620679505081
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Conference Paper: Maskgan: Towards diverse and interactive facial image manipulation
Title | Maskgan: Towards diverse and interactive facial image manipulation |
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Authors | |
Keywords | face recognition image resolution learning (artificial intelligence) neural nets |
Issue Date | 2020 |
Publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147 |
Citation | Proceedings of IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, USA, 14-19 June 2020, p. 5548-5557 How to Cite? |
Abstract | Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBST). Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results. EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs. Specifically, it introduces dual-editing consistency as the auxiliary supervision signal. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ. MaskGAN is comprehensively evaluated on two challenging tasks: attribute transfer and style copy, demonstrating superior performance over other state-of-the-art methods. The code, models, and dataset are available at https://github.com/switchablenorms/CelebAMask-HQ. |
Description | Session: Poster 2.1 — 3D From Multiview and Sensors; Face, Gesture, and Body Pose; Image and Video Synthesis - Poster no. 69 ; Paper ID 2297 |
Persistent Identifier | http://hdl.handle.net/10722/284166 |
ISSN | 2023 SCImago Journal Rankings: 10.331 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lee, C | - |
dc.contributor.author | Liu, Z | - |
dc.contributor.author | Wu, L | - |
dc.contributor.author | Luo, P | - |
dc.date.accessioned | 2020-07-20T05:56:36Z | - |
dc.date.available | 2020-07-20T05:56:36Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, USA, 14-19 June 2020, p. 5548-5557 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10722/284166 | - |
dc.description | Session: Poster 2.1 — 3D From Multiview and Sensors; Face, Gesture, and Body Pose; Image and Video Synthesis - Poster no. 69 ; Paper ID 2297 | - |
dc.description.abstract | Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBST). Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results. EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs. Specifically, it introduces dual-editing consistency as the auxiliary supervision signal. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ. MaskGAN is comprehensively evaluated on two challenging tasks: attribute transfer and style copy, demonstrating superior performance over other state-of-the-art methods. The code, models, and dataset are available at https://github.com/switchablenorms/CelebAMask-HQ. | - |
dc.language | eng | - |
dc.publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147 | - |
dc.relation.ispartof | IEEE Conference on Computer Vision and Pattern Recognition. Proceedings | - |
dc.rights | IEEE Conference on Computer Vision and Pattern Recognition. Proceedings. Copyright © IEEE Computer Society. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | face recognition | - |
dc.subject | image resolution | - |
dc.subject | learning (artificial intelligence) | - |
dc.subject | neural nets | - |
dc.title | Maskgan: Towards diverse and interactive facial image manipulation | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Luo, P: pluo@hku.hk | - |
dc.identifier.authority | Luo, P=rp02575 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CVPR42600.2020.00559 | - |
dc.identifier.scopus | eid_2-s2.0-85093100763 | - |
dc.identifier.hkuros | 311027 | - |
dc.identifier.spage | 5548 | - |
dc.identifier.epage | 5557 | - |
dc.identifier.isi | WOS:000620679505081 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1063-6919 | - |