514 research outputs found
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem
GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation
Automatic artistic text generation is an emerging topic which receives
increasing attention due to its wide applications. The artistic text can be
divided into three components, content, font, and texture, respectively.
Existing artistic text generation models usually focus on manipulating one
aspect of the above components, which is a sub-optimal solution for
controllable general artistic text generation. To remedy this issue, we propose
a novel approach, namely GenText, to achieve general artistic text style
transfer by separably migrating the font and texture styles from the different
source images to the target images in an unsupervised manner. Specifically, our
current work incorporates three different stages, stylization, destylization,
and font transfer, respectively, into a unified platform with a single powerful
encoder network and two separate style generator networks, one for font
transfer, the other for stylization and destylization. The destylization stage
first extracts the font style of the font reference image, then the font
transfer stage generates the target content with the desired font style.
Finally, the stylization stage renders the resulted font image with respect to
the texture style in the reference image. Moreover, considering the difficult
data acquisition of paired artistic text images, our model is designed under
the unsupervised setting, where all stages can be effectively optimized from
unpaired data. Qualitative and quantitative results are performed on artistic
text benchmarks, which demonstrate the superior performance of our proposed
model. The code with models will become publicly available in the future
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose semantic region-adaptive normalization (SEAN), a simple but
effective building block for Generative Adversarial Networks conditioned on
segmentation masks that describe the semantic regions in the desired output
image. Using SEAN normalization, we can build a network architecture that can
control the style of each semantic region individually, e.g., we can specify
one style reference image per region. SEAN is better suited to encode,
transfer, and synthesize style than the best previous method in terms of
reconstruction quality, variability, and visual quality. We evaluate SEAN on
multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than
the current state of the art. SEAN also pushes the frontier of interactive
image editing. We can interactively edit images by changing segmentation masks
or the style for any given region. We can also interpolate styles from two
reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available
at https://youtu.be/0Vbj9xFgoU
Controllable Multi-domain Semantic Artwork Synthesis
We present a novel framework for multi-domain synthesis of artwork from
semantic layouts. One of the main limitations of this challenging task is the
lack of publicly available segmentation datasets for art synthesis. To address
this problem, we propose a dataset, which we call ArtSem, that contains 40,000
images of artwork from 4 different domains with their corresponding semantic
label maps. We generate the dataset by first extracting semantic maps from
landscape photography and then propose a conditional Generative Adversarial
Network (GAN)-based approach to generate high-quality artwork from the semantic
maps without necessitating paired training data. Furthermore, we propose an
artwork synthesis model that uses domain-dependent variational encoders for
high-quality multi-domain synthesis. The model is improved and complemented
with a simple but effective normalization method, based on normalizing both the
semantic and style jointly, which we call Spatially STyle-Adaptive
Normalization (SSTAN). In contrast to previous methods that only take semantic
layout as input, our model is able to learn a joint representation of both
style and semantic information, which leads to better generation quality for
synthesizing artistic images. Results indicate that our model learns to
separate the domains in the latent space, and thus, by identifying the
hyperplanes that separate the different domains, we can also perform
fine-grained control of the synthesized artwork. By combining our proposed
dataset and approach, we are able to generate user-controllable artwork that is
of higher quality than existingComment: 15 pages, accepted by CVMJ, to appea
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Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem
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