6,856 research outputs found
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
Mean value coordinates–based caricature and expression synthesis
We present a novel method for caricature synthesis based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face pair for frontal and 3D caricature synthesis. This technique only requires one or a small number of exemplar pairs and a natural frontal face image training set, while the system can transfer the style of the exemplar pair across individuals. Further exaggeration can be fulfilled in a controllable way. Our method is further applied to facial expression transfer, interpolation, and exaggeration, which are applications of expression editing. Additionally, we have extended our approach to 3D caricature synthesis based on the 3D version of MVC. With experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized
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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