84 research outputs found
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color,
and texture. Previous image synthesis methods can be controlled by sketch and
color strokes but we are the first to examine texture control. We allow a user
to place a texture patch on a sketch at arbitrary locations and scales to
control the desired output texture. Our generative network learns to synthesize
objects consistent with these texture suggestions. To achieve this, we develop
a local texture loss in addition to adversarial and content loss to train the
generative network. We conduct experiments using sketches generated from real
images and textures sampled from a separate texture database and results show
that our proposed algorithm is able to generate plausible images that are
faithful to user controls. Ablation studies show that our proposed pipeline can
generate more realistic images than adapting existing methods directly.Comment: CVPR 2018 spotligh
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high quality, especially at high-resolution
and photorealism. In this paper, we present Discriminative Region Proposal
Adversarial Networks (DRPAN) for high-quality image-to-image translation. We
decompose the procedure of image-to-image translation task into three iterated
steps, first is to generate an image with global structure but some local
artifacts (via GAN), second is using our DRPnet to propose the most fake region
from the generated image, and third is to implement "image inpainting" on the
most fake region for more realistic result through a reviser, so that the
system (DRPAN) can be gradually optimized to synthesize images with more
attention on the most artifact local part. Experiments on a variety of
image-to-image translation tasks and datasets validate that our method
outperforms state-of-the-arts for producing high-quality translation results in
terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201
Pixelated Semantic Colorization
While many image colorization algorithms have recently shown the capability
of producing plausible color versions from gray-scale photographs, they still
suffer from limited semantic understanding. To address this shortcoming, we
propose to exploit pixelated object semantics to guide image colorization. The
rationale is that human beings perceive and distinguish colors based on the
semantic categories of objects. Starting from an autoregressive model, we
generate image color distributions, from which diverse colored results are
sampled. We propose two ways to incorporate object semantics into the
colorization model: through a pixelated semantic embedding and a pixelated
semantic generator. Specifically, the proposed convolutional neural network
includes two branches. One branch learns what the object is, while the other
branch learns the object colors. The network jointly optimizes a color
embedding loss, a semantic segmentation loss and a color generation loss, in an
end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that
our network, when trained with semantic segmentation labels, produces more
realistic and finer results compared to the colorization state-of-the-art
A Survey for Graphic Design Intelligence
Graphic design is an effective language for visual communication. Using
complex composition of visual elements (e.g., shape, color, font) guided by
design principles and aesthetics, design helps produce more visually-appealing
content. The creation of a harmonious design requires carefully selecting and
combining different visual elements, which can be challenging and
time-consuming. To expedite the design process, emerging AI techniques have
been proposed to automatize tedious tasks and facilitate human creativity.
However, most current works only focus on specific tasks targeting at different
scenarios without a high-level abstraction. This paper aims to provide a
systematic overview of graphic design intelligence and summarize literature in
the taxonomy of representation, understanding and generation. Specifically we
consider related works for individual visual elements as well as the overall
design composition. Furthermore, we highlight some of the potential directions
for future explorations.Comment: 10 pages, 2 figure
- …