22 research outputs found
Fader Networks: Manipulating Images by Sliding Attributes
This paper introduces a new encoder-decoder architecture that is trained to
reconstruct images by disentangling the salient information of the image and
the values of attributes directly in the latent space. As a result, after
training, our model can generate different realistic versions of an input image
by varying the attribute values. By using continuous attribute values, we can
choose how much a specific attribute is perceivable in the generated image.
This property could allow for applications where users can modify an image
using sliding knobs, like faders on a mixing console, to change the facial
expression of a portrait, or to update the color of some objects. Compared to
the state-of-the-art which mostly relies on training adversarial networks in
pixel space by altering attribute values at train time, our approach results in
much simpler training schemes and nicely scales to multiple attributes. We
present evidence that our model can significantly change the perceived value of
the attributes while preserving the naturalness of images.Comment: NIPS 201
NAM: Non-Adversarial Unsupervised Domain Mapping
Several methods were recently proposed for the task of translating images
between domains without prior knowledge in the form of correspondences. The
existing methods apply adversarial learning to ensure that the distribution of
the mapped source domain is indistinguishable from the target domain, which
suffers from known stability issues. In addition, most methods rely heavily on
`cycle' relationships between the domains, which enforce a one-to-one mapping.
In this work, we introduce an alternative method: Non-Adversarial Mapping
(NAM), which separates the task of target domain generative modeling from the
cross-domain mapping task. NAM relies on a pre-trained generative model of the
target domain, and aligns each source image with an image synthesized from the
target domain, while jointly optimizing the domain mapping function. It has
several key advantages: higher quality and resolution image translations,
simpler and more stable training and reusable target models. Extensive
experiments are presented validating the advantages of our method.Comment: ECCV 201
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
Synthesizing face sketches from real photos and its inverse have many
applications. However, photo/sketch synthesis remains a challenging problem due
to the fact that photo and sketch have different characteristics. In this work,
we consider this task as an image-to-image translation problem and explore the
recently popular generative models (GANs) to generate high-quality realistic
photos from sketches and sketches from photos. Recent GAN-based methods have
shown promising results on image-to-image translation problems and
photo-to-sketch synthesis in particular, however, they are known to have
limited abilities in generating high-resolution realistic images. To this end,
we propose a novel synthesis framework called Photo-Sketch Synthesis using
Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution
to high resolution images in an adversarial way. The hidden layers of the
generator are supervised to first generate lower resolution images followed by
implicit refinement in the network to generate higher resolution images.
Furthermore, since photo-sketch synthesis is a coupled/paired translation
problem, we leverage the pair information using CycleGAN framework. Both Image
Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to
demonstrate the superior performance of our framework in comparison to existing
state-of-the-art solutions. Code available at:
https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face
& Gesture Recognition (FG 2018)(Oral