Exp-GAN: 3D-Aware Facial Image Generation with Expression Control
- Title
- Exp-GAN: 3D-Aware Facial Image Generation with Expression Control
- Authors
- LEE, YEONKYEONG; CHOI, TAEHO; GO, HYUNSUNG; LEE, HYUNJOON; CHO, SUNGHYUN; KIM, JUNHO
- Date Issued
- 2022-12-07
- Publisher
- Asian Federation of Computer Vision
- Abstract
- This paper introduces Exp-GAN, a 3D-aware facial image generator with explicit control of facial expressions. Unlike previous 3D-aware GANs, Exp-GAN supports fine-grained control over facial shapes and expressions disentangled from poses. To this ends, we propose a novel hybrid approach that adopts a 3D morphable model (3DMM) with neural textures for the facial region and a neural radiance field (NeRF) for non-facial regions with multi-view consistency. The 3DMM allows fine-grained control over facial expressions, whereas the NeRF contains volumetric features for the non-facial regions. The two features, generated separately, are combined seamlessly with our depth-based integration method that integrates the two complementary features through volume rendering. We also propose a training scheme that encourages generated images to reflect control over shapes and expressions faithfully. Experimental results show that the proposed approach successfully synthesizes realistic view-consistent face images with fine-grained controls. Code is available at https://github.com/kakaobrain/expgan.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/114430
- ISSN
- 0302-9743
- Article Type
- Conference
- Citation
- ACCV, page. 151 - 167, 2022-12-07
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- There are no files associated with this item.
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