3,232 research outputs found
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
1C Action / Pose / Biometric - paper no. 3
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
1C Action / Pose / Biometric - paper no. 3
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
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
Synthesizing face sketches from real photos and its inverse have many
applications. However, photo/sketch synthesis remains a challenging problem due
to the fact that photo and sketch have different characteristics. In this work,
we consider this task as an image-to-image translation problem and explore the
recently popular generative models (GANs) to generate high-quality realistic
photos from sketches and sketches from photos. Recent GAN-based methods have
shown promising results on image-to-image translation problems and
photo-to-sketch synthesis in particular, however, they are known to have
limited abilities in generating high-resolution realistic images. To this end,
we propose a novel synthesis framework called Photo-Sketch Synthesis using
Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution
to high resolution images in an adversarial way. The hidden layers of the
generator are supervised to first generate lower resolution images followed by
implicit refinement in the network to generate higher resolution images.
Furthermore, since photo-sketch synthesis is a coupled/paired translation
problem, we leverage the pair information using CycleGAN framework. Both Image
Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to
demonstrate the superior performance of our framework in comparison to existing
state-of-the-art solutions. Code available at:
https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face
& Gesture Recognition (FG 2018)(Oral
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