Corpus ID: 222141601

SMILE: Semantically-guided Multi-attribute Image and Layout Editing

@article{Romero2020SMILESM,
  title={SMILE: Semantically-guided Multi-attribute Image and Layout Editing},
  author={Andr{\'e}s Romero and L. Gool and R. Timofte},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.02315}
}
Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within a transformation is a very challenging task due to the multiple and mutually-inclusive nature of the facial images, where different labels (eyeglasses, hats, hair, identity, etc.) can co-exist at the same time. Several works address this issue either by exploiting the modality of each domain/attribute using a conditional… Expand
GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
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References

SHOWING 1-10 OF 52 REFERENCES
MulGAN: Facial Attribute Editing by Exemplar
TLDR
A novel model structure is designed to enhance attribute transfer capabilities by exemplars while improve the quality of the generated image by directly applying the attribute labels constraint to the predefined region of the latent feature space. Expand
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes
TLDR
A novel model is proposed which receives two images of opposite attributes as inputs and can transfer exactly the same type of attributes from one image to another by exchanging certain part of their encodings. Expand
AttGAN: Facial Attribute Editing by Only Changing What You Want
TLDR
The proposed method is extended for attribute style manipulation in an unsupervised manner and outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved. Expand
GANimation: Anatomically-aware Facial Animation from a Single Image
TLDR
A novel GAN conditioning scheme based on Action Units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression, and proposes a fully unsupervised strategy to train the model, that only requires images annotated with their activated AUs. Expand
Fader Networks: Manipulating Images by Sliding Attributes
TLDR
A new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space is introduced, which results in much simpler training schemes and nicely scales to multiple attributes. Expand
Interpreting the Latent Space of GANs for Semantic Face Editing
TLDR
This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations. Expand
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
TLDR
A new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented, which significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing. Expand
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
TLDR
RelGAN is a new method for multi-domain image-to-image translation that is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other attributes. Expand
SMIT: Stochastic Multi-Label Image-to-Image Translation
TLDR
This work proposes a joint framework of diversity and multi-mapping image-to-image translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image. Expand
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually gives rise toExpand
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