442 research outputs found
Local Facial Attribute Transfer through Inpainting
The term attribute transfer refers to the tasks of altering images in such a
way, that the semantic interpretation of a given input image is shifted towards
an intended direction, which is quantified by semantic attributes. Prominent
example applications are photo realistic changes of facial features and
expressions, like changing the hair color, adding a smile, enlarging the nose
or altering the entire context of a scene, like transforming a summer landscape
into a winter panorama. Recent advances in attribute transfer are mostly based
on generative deep neural networks, using various techniques to manipulate
images in the latent space of the generator. In this paper, we present a novel
method for the common sub-task of local attribute transfers, where only parts
of a face have to be altered in order to achieve semantic changes (e.g.
removing a mustache). In contrast to previous methods, where such local changes
have been implemented by generating new (global) images, we propose to
formulate local attribute transfers as an inpainting problem. Removing and
regenerating only parts of images, our Attribute Transfer Inpainting Generative
Adversarial Network (ATI-GAN) is able to utilize local context information,
resulting in visually sound results
Geometry-Aware Face Completion and Editing
Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked region. Firstly, a facial geometry estimator is learned to
estimate facial landmark heatmaps and parsing maps from the unmasked face
image. Then, an encoder-decoder structure generator serves to complete a face
image and disentangle its mask areas conditioned on both the masked face image
and the estimated facial geometry images. Besides, since low-rank property
exists in manually labeled masks, a low-rank regularization term is imposed on
the disentangled masks, enforcing our completion network to manage occlusion
area with various shape and size. Furthermore, our network can generate diverse
results from the same masked input by modifying estimated facial geometry,
which provides a flexible mean to edit the completed face appearance. Extensive
experimental results qualitatively and quantitatively demonstrate that our
network is able to generate visually pleasing face completion results and edit
face attributes as well
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
FaceShop: Deep Sketch-based Face Image Editing
We present a novel system for sketch-based face image editing, enabling users
to edit images intuitively by sketching a few strokes on a region of interest.
Our interface features tools to express a desired image manipulation by
providing both geometry and color constraints as user-drawn strokes. As an
alternative to the direct user input, our proposed system naturally supports a
copy-paste mode, which allows users to edit a given image region by using parts
of another exemplar image without the need of hand-drawn sketching at all. The
proposed interface runs in real-time and facilitates an interactive and
iterative workflow to quickly express the intended edits. Our system is based
on a novel sketch domain and a convolutional neural network trained end-to-end
to automatically learn to render image regions corresponding to the input
strokes. To achieve high quality and semantically consistent results we train
our neural network on two simultaneous tasks, namely image completion and image
translation. To the best of our knowledge, we are the first to combine these
two tasks in a unified framework for interactive image editing. Our results
show that the proposed sketch domain, network architecture, and training
procedure generalize well to real user input and enable high quality synthesis
results without additional post-processing.Comment: 13 pages, 20 figure
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