73 research outputs found
Intelligent visual media processing: when graphics meets vision
The computer graphics and computer vision communities have been working closely together in recent
years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media
around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the
Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing
tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data;
iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and
3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics
and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey
recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover
research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest
possible further research directions
Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline
Designers need to consider not only perceptual effectiveness but also visual
styles when creating an infographic. This process can be difficult and time
consuming for professional designers, not to mention non-expert users, leading
to the demand for automated infographics design. As a first step, we focus on
timeline infographics, which have been widely used for centuries. We contribute
an end-to-end approach that automatically extracts an extensible timeline
template from a bitmap image. Our approach adopts a deconstruction and
reconstruction paradigm. At the deconstruction stage, we propose a multi-task
deep neural network that simultaneously parses two kinds of information from a
bitmap timeline: 1) the global information, i.e., the representation, scale,
layout, and orientation of the timeline, and 2) the local information, i.e.,
the location, category, and pixels of each visual element on the timeline. At
the reconstruction stage, we propose a pipeline with three techniques, i.e.,
Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an
extensible template from the infographic, by utilizing the deconstruction
results. To evaluate the effectiveness of our approach, we synthesize a
timeline dataset (4296 images) and collect a real-world timeline dataset (393
images) from the Internet. We first report quantitative evaluation results of
our approach over the two datasets. Then, we present examples of automatically
extracted templates and timelines automatically generated based on these
templates to qualitatively demonstrate the performance. The results confirm
that our approach can effectively extract extensible templates from real-world
timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach,
Timeline Infographics, Multi-task Mode
VITON: An Image-based Virtual Try-on Network
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models
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