363 research outputs found
Attribute-Guided Face Generation Using Conditional CycleGAN
We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method.Comment: ECCV 201
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Cross-domain synthesizing realistic faces to learn deep models has attracted
increasing attention for facial expression analysis as it helps to improve the
performance of expression recognition accuracy despite having small number of
real training images. However, learning from synthetic face images can be
problematic due to the distribution discrepancy between low-quality synthetic
images and real face images and may not achieve the desired performance when
the learned model applies to real world scenarios. To this end, we propose a
new attribute guided face image synthesis to perform a translation between
multiple image domains using a single model. In addition, we adopt the proposed
model to learn from synthetic faces by matching the feature distributions
between different domains while preserving each domain's characteristics. We
evaluate the effectiveness of the proposed approach on several face datasets on
generating realistic face images. We demonstrate that the expression
recognition performance can be enhanced by benefiting from our face synthesis
model. Moreover, we also conduct experiments on a near-infrared dataset
containing facial expression videos of drivers to assess the performance using
in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note:
substantial text overlap with arXiv:1905.0028
Learn to synthesize and synthesize to learn
Attribute guided face image synthesis aims to manipulate attributes on a face
image. Most existing methods for image-to-image translation can either perform
a fixed translation between any two image domains using a single attribute or
require training data with the attributes of interest for each subject.
Therefore, these methods could only train one specific model for each pair of
image domains, which limits their ability in dealing with more than two
domains. Another disadvantage of these methods is that they often suffer from
the common problem of mode collapse that degrades the quality of the generated
images. To overcome these shortcomings, we propose attribute guided face image
generation method using a single model, which is capable to synthesize multiple
photo-realistic face images conditioned on the attributes of interest. In
addition, we adopt the proposed model to increase the realism of the simulated
face images while preserving the face characteristics. Compared to existing
models, synthetic face images generated by our method present a good
photorealistic quality on several face datasets. Finally, we demonstrate that
generated facial images can be used for synthetic data augmentation, and
improve the performance of the classifier used for facial expression
recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201
Recovering Faces from Portraits with Auxiliary Facial Attributes
Recovering a photorealistic face from an artistic portrait is a challenging
task since crucial facial details are often distorted or completely lost in
artistic compositions. To handle this loss, we propose an Attribute-guided Face
Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and
a Discriminative Network (DN). FRN consists of an autoencoder with residual
block-embedded skip-connections and incorporates facial attribute vectors into
the feature maps of input portraits at the bottleneck of the autoencoder. DN
has multiple convolutional and fully-connected layers, and its role is to
enforce FRN to generate authentic face images with corresponding facial
attributes dictated by the input attribute vectors. %Leveraging on the spatial
transformer networks, FRN automatically compensates for misalignments of
portraits. % and generates aligned face images. For the preservation of
identities, we impose the recovered and ground-truth faces to share similar
visual features. Specifically, DN determines whether the recovered image looks
like a real face and checks if the facial attributes extracted from the
recovered image are consistent with given attributes. %Our method can recover
high-quality photorealistic faces from unaligned portraits while preserving the
identity of the face images as well as it can reconstruct a photorealistic face
image with a desired set of attributes. Our method can recover photorealistic
identity-preserving faces with desired attributes from unseen stylized
portraits, artistic paintings, and hand-drawn sketches. On large-scale
synthesized and sketch datasets, we demonstrate that our face recovery method
achieves state-of-the-art results.Comment: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is made to tackle the inverse problem of synthesizing faces from their
respective landmarks. The primary aim of this work is to demonstrate that
information preserved by landmarks (gender in particular) can be further
accentuated by leveraging generative models to synthesize corresponding faces.
Though the problem is particularly challenging due to its ill-posed nature, we
believe that successful synthesis will enable several applications such as
boosting performance of high-level face related tasks using landmark points and
performing dataset augmentation. To this end, a novel face-synthesis method
known as Gender Preserving Generative Adversarial Network (GP-GAN) that is
guided by adversarial loss, perceptual loss and a gender preserving loss is
presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN
that leverages advantages of U-Net and DenseNet architectures. Extensive
experiments and comparison with recent methods are performed to verify the
effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International
Conference on Pattern Recognition (ICPR2018
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