Face Attribute Invertion
arXiv:2001.04665
Abstract
Manipulating human facial images between two domains is an important and interesting problem. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs. In this paper, we proposed a novel self-perception method based on GANs for automatical face attribute inverse. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images.
8 pages, 3 figures
References in corpus (5)
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Invertible Conditional GANs for image editing
- Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
- Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture
- Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing