684 research outputs found
Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network
Modifying the facial images with desired attributes is important, though
challenging tasks in computer vision, where it aims to modify single or
multiple attributes of the face image. Some of the existing methods are either
based on attribute independent approaches where the modification is done in the
latent representation or attribute dependent approaches. The attribute
independent methods are limited in performance as they require the desired
paired data for changing the desired attributes. Secondly, the attribute
independent constraint may result in the loss of information and, hence, fail
in generating the required attributes in the face image. In contrast, the
attribute dependent approaches are effective as these approaches are capable of
modifying the required features along with preserving the information in the
given image. However, attribute dependent approaches are sensitive and require
a careful model design in generating high-quality results. To address this
problem, we propose an attribute dependent face modification approach. The
proposed approach is based on two generators and two discriminators that
utilize the binary as well as the real representation of the attributes and, in
return, generate high-quality attribute modification results. Experiments on
the CelebA dataset show that our method effectively performs the multiple
attribute editing with preserving other facial details intactly
Style Separation and Synthesis via Generative Adversarial Networks
Style synthesis attracts great interests recently, while few works focus on
its dual problem "style separation". In this paper, we propose the Style
Separation and Synthesis Generative Adversarial Network (S3-GAN) to
simultaneously implement style separation and style synthesis on object
photographs of specific categories. Based on the assumption that the object
photographs lie on a manifold, and the contents and styles are independent, we
employ S3-GAN to build mappings between the manifold and a latent vector space
for separating and synthesizing the contents and styles. The S3-GAN consists of
an encoder network, a generator network, and an adversarial network. The
encoder network performs style separation by mapping an object photograph to a
latent vector. Two halves of the latent vector represent the content and style,
respectively. The generator network performs style synthesis by taking a
concatenated vector as input. The concatenated vector contains the style half
vector of the style target image and the content half vector of the content
target image. Once obtaining the images from the generator network, an
adversarial network is imposed to generate more photo-realistic images.
Experiments on CelebA and UT Zappos 50K datasets demonstrate that the S3-GAN
has the capacity of style separation and synthesis simultaneously, and could
capture various styles in a single model
- …