25,801 research outputs found
Semantic Photo Manipulation with a Generative Image Prior
Despite the recent success of GANs in synthesizing images conditioned on
inputs such as a user sketch, text, or semantic labels, manipulating the
high-level attributes of an existing natural photograph with GANs is
challenging for two reasons. First, it is hard for GANs to precisely reproduce
an input image. Second, after manipulation, the newly synthesized pixels often
do not fit the original image. In this paper, we address these issues by
adapting the image prior learned by GANs to image statistics of an individual
image. Our method can accurately reconstruct the input image and synthesize new
content, consistent with the appearance of the input image. We demonstrate our
interactive system on several semantic image editing tasks, including
synthesizing new objects consistent with background, removing unwanted objects,
and changing the appearance of an object. Quantitative and qualitative
comparisons against several existing methods demonstrate the effectiveness of
our method.Comment: SIGGRAPH 201
Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
This paper addresses the problem of interpolating visual textures. We
formulate this problem by requiring (1) by-example controllability and (2)
realistic and smooth interpolation among an arbitrary number of texture
samples. To solve it we propose a neural network trained simultaneously on a
reconstruction task and a generation task, which can project texture examples
onto a latent space where they can be linearly interpolated and projected back
onto the image domain, thus ensuring both intuitive control and realistic
results. We show our method outperforms a number of baselines according to a
comprehensive suite of metrics as well as a user study. We further show several
applications based on our technique, which include texture brush, texture
dissolve, and animal hybridization.Comment: Accepted to CVPR'1
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