437 research outputs found
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation
performance over the past few years. However, they require large numbers of
annotations. To address this problem, we propose a novel generative adversarial
network utilizing weak supervision in the form of pairwise comparisons (PC-GAN)
for image attribute editing. In the light of Bayesian uncertainty estimation
and noise-tolerant adversarial training, PC-GAN can estimate attribute rating
efficiently and demonstrate robust performance in noise resistance. Through
extensive experiments, we show both qualitatively and quantitatively that
PC-GAN performs comparably with fully-supervised methods and outperforms
unsupervised baselines.Comment: Accepted for spotlight at AAAI-2
POCE: Pose-Controllable Expression Editing
Facial expression editing has attracted increasing attention with the advance
of deep neural networks in recent years. However, most existing methods suffer
from compromised editing fidelity and limited usability as they either ignore
pose variations (unrealistic editing) or require paired training data (not easy
to collect) for pose controls. This paper presents POCE, an innovative
pose-controllable expression editing network that can generate realistic facial
expressions and head poses simultaneously with just unpaired training images.
POCE achieves the more accessible and realistic pose-controllable expression
editing by mapping face images into UV space, where facial expressions and head
poses can be disentangled and edited separately. POCE has two novel designs.
The first is self-supervised UV completion that allows to complete UV maps
sampled under different head poses, which often suffer from self-occlusions and
missing facial texture. The second is weakly-supervised UV editing that allows
to generate new facial expressions with minimal modification of facial
identity, where the synthesized expression could be controlled by either an
expression label or directly transplanted from a reference UV map via feature
transfer. Extensive experiments show that POCE can learn from unpaired face
images effectively, and the learned model can generate realistic and
high-fidelity facial expressions under various new poses
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
While existing makeup style transfer models perform an image synthesis whose
results cannot be explicitly controlled, the ability to modify makeup color
continuously is a desirable property for virtual try-on applications. We
propose a new formulation for the makeup style transfer task, with the
objective to learn a color controllable makeup style synthesis. We introduce
CA-GAN, a generative model that learns to modify the color of specific objects
(e.g. lips or eyes) in the image to an arbitrary target color while preserving
background. Since color labels are rare and costly to acquire, our method
leverages weakly supervised learning for conditional GANs. This enables to
learn a controllable synthesis of complex objects, and only requires a weak
proxy of the image attribute that we desire to modify. Finally, we present for
the first time a quantitative analysis of makeup style transfer and color
control performance
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