16,204 research outputs found
Exploring the structure of a real-time, arbitrary neural artistic stylization network
In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference
(BMVC) 201
TET-GAN: Text Effects Transfer via Stylization and Destylization
Text effects transfer technology automatically makes the text dramatically
more impressive. However, previous style transfer methods either study the
model for general style, which cannot handle the highly-structured text effects
along the glyph, or require manual design of subtle matching criteria for text
effects. In this paper, we focus on the use of the powerful representation
abilities of deep neural features for text effects transfer. For this purpose,
we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a
stylization subnetwork and a destylization subnetwork. The key idea is to train
our network to accomplish both the objective of style transfer and style
removal, so that it can learn to disentangle and recombine the content and
style features of text effects images. To support the training of our network,
we propose a new text effects dataset with as much as 64 professionally
designed styles on 837 characters. We show that the disentangled feature
representations enable us to transfer or remove all these styles on arbitrary
glyphs using one network. Furthermore, the flexible network design empowers
TET-GAN to efficiently extend to a new text style via one-shot learning where
only one example is required. We demonstrate the superiority of the proposed
method in generating high-quality stylized text over the state-of-the-art
methods.Comment: Accepted by AAAI 2019. Code and dataset will be available at
http://www.icst.pku.edu.cn/struct/Projects/TETGAN.htm
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