208 research outputs found
Laplacian-Steered Neural Style Transfer
Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to
synthesize a new image that retains the high-level structure of a content
image, rendered in the low-level texture of a style image. This is achieved by
constraining the new image to have high-level CNN features similar to the
content image, and lower-level CNN features similar to the style image. However
in the traditional optimization objective, low-level features of the content
image are absent, and the low-level features of the style image dominate the
low-level detail structures of the new image. Hence in the synthesized image,
many details of the content image are lost, and a lot of inconsistent and
unpleasing artifacts appear. As a remedy, we propose to steer image synthesis
with a novel loss function: the Laplacian loss. The Laplacian matrix
("Laplacian" in short), produced by a Laplacian operator, is widely used in
computer vision to detect edges and contours. The Laplacian loss measures the
difference of the Laplacians, and correspondingly the difference of the detail
structures, between the content image and a new image. It is flexible and
compatible with the traditional style transfer constraints. By incorporating
the Laplacian loss, we obtain a new optimization objective for neural style
transfer named Lapstyle. Minimizing this objective will produce a stylized
image that better preserves the detail structures of the content image and
eliminates the artifacts. Experiments show that Lapstyle produces more
appealing stylized images with less artifacts, without compromising their
"stylishness".Comment: Accepted by the ACM Multimedia Conference (MM) 2017. 9 pages, 65
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Neural Smoke Stylization with Color Transfer
Artistically controlling fluid simulations requires a large amount of manual
work by an artist. The recently presented transportbased neural style transfer
approach simplifies workflows as it transfers the style of arbitrary input
images onto 3D smoke simulations. However, the method only modifies the shape
of the fluid but omits color information. In this work, we therefore extend the
previous approach to obtain a complete pipeline for transferring shape and
color information onto 2D and 3D smoke simulations with neural networks. Our
results demonstrate that our method successfully transfers colored style
features consistently in space and time to smoke data for different input
textures.Comment: Submitted to Eurographics202
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