4 research outputs found
Generate Identity-Preserving Faces by Generative Adversarial Networks
Generating identity-preserving faces aims to generate various face images
keeping the same identity given a target face image. Although considerable
generative models have been developed in recent years, it is still challenging
to simultaneously acquire high quality of facial images and preserve the
identity. Here we propose a compelling method using generative adversarial
networks (GAN). Concretely, we leverage the generator of trained GAN to
generate plausible faces and FaceNet as an identity-similarity discriminator to
ensure the identity. Experimental results show that our method is qualified to
generate both plausible and identity-preserving faces with high quality. In
addition, our method provides a universal framework which can be realized in
various ways by combining different face generators and identity-similarity
discriminator.Comment: 9 page
Face Translation between Images and Videos using Identity-aware CycleGAN
This paper presents a new problem of unpaired face translation between images
and videos, which can be applied to facial video prediction and enhancement. In
this problem there exist two major technical challenges: 1) designing a robust
translation model between static images and dynamic videos, and 2) preserving
facial identity during image-video translation. To address such two problems,
we generalize the state-of-the-art image-to-image translation network
(Cycle-Consistent Adversarial Networks) to the image-to-video/video-to-image
translation context by exploiting a image-video translation model and an
identity preservation model. In particular, we apply the state-of-the-art
Wasserstein GAN technique to the setting of image-video translation for better
convergence, and we meanwhile introduce a face verificator to ensure the
identity. Experiments on standard image/video face datasets demonstrate the
effectiveness of the proposed model in both terms of qualitative and
quantitative evaluations
Flipped-Adversarial AutoEncoders
We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously
trains a generative model G that maps an arbitrary latent code distribution to
a data distribution and an encoder E that embodies an "inverse mapping" that
encodes a data sample into a latent code vector. Unlike previous hybrid
approaches that leverage adversarial training criterion in constructing
autoencoders, FAAE minimizes re-encoding errors in the latent space and
exploits adversarial criterion in the data space. Experimental evaluations
demonstrate that the proposed framework produces sharper reconstructed images
while at the same time enabling inference that captures rich semantic
representation of data
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Generating high fidelity identity-preserving faces with different facial
attributes has a wide range of applications. Although a number of generative
models have been developed to tackle this problem, there is still much room for
further improvement.In paticular, the current solutions usually ignore the
perceptual information of images, which we argue that it benefits the output of
a high-quality image while preserving the identity information, especially in
facial attributes learning area.To this end, we propose to train GAN
iteratively via regularizing the min-max process with an integrated loss, which
includes not only the per-pixel loss but also the perceptual loss. In contrast
to the existing methods only deal with either image generation or
transformation, our proposed iterative architecture can achieve both of them.
Experiments on the multi-label facial dataset CelebA demonstrate that the
proposed model has excellent performance on recognizing multiple attributes,
generating a high-quality image, and transforming image with controllable
attributes