1,048 research outputs found
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
We present a new method for synthesizing high-resolution photo-realistic
images from semantic label maps using conditional generative adversarial
networks (conditional GANs). Conditional GANs have enabled a variety of
applications, but the results are often limited to low-resolution and still far
from realistic. In this work, we generate 2048x1024 visually appealing results
with a novel adversarial loss, as well as new multi-scale generator and
discriminator architectures. Furthermore, we extend our framework to
interactive visual manipulation with two additional features. First, we
incorporate object instance segmentation information, which enables object
manipulations such as removing/adding objects and changing the object category.
Second, we propose a method to generate diverse results given the same input,
allowing users to edit the object appearance interactively. Human opinion
studies demonstrate that our method significantly outperforms existing methods,
advancing both the quality and the resolution of deep image synthesis and
editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example
Geometry-Aware Face Completion and Editing
Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked region. Firstly, a facial geometry estimator is learned to
estimate facial landmark heatmaps and parsing maps from the unmasked face
image. Then, an encoder-decoder structure generator serves to complete a face
image and disentangle its mask areas conditioned on both the masked face image
and the estimated facial geometry images. Besides, since low-rank property
exists in manually labeled masks, a low-rank regularization term is imposed on
the disentangled masks, enforcing our completion network to manage occlusion
area with various shape and size. Furthermore, our network can generate diverse
results from the same masked input by modifying estimated facial geometry,
which provides a flexible mean to edit the completed face appearance. Extensive
experimental results qualitatively and quantitatively demonstrate that our
network is able to generate visually pleasing face completion results and edit
face attributes as well
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