SMILE: Semantically-guided Multi-attribute Image and Layout Editing
- URL: http://arxiv.org/abs/2010.02315v1
- Date: Mon, 5 Oct 2020 20:15:21 GMT
- Title: SMILE: Semantically-guided Multi-attribute Image and Layout Editing
- Authors: Andr\'es Romero, Luc Van Gool, Radu Timofte
- Abstract summary: Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs)
We present a multimodal representation that handles all attributes, be it guided by random noise or images, while only using the underlying domain information of the target domain.
Our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space.
- Score: 154.69452301122175
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 random vector noise, or extracting
the modality from an exemplary image. However, existing methods cannot handle
both random and reference transformations for multiple attributes, which limits
the generality of the solutions. In this paper, we successfully exploit a
multimodal representation that handles all attributes, be it guided by random
noise or exemplar images, while only using the underlying domain information of
the target domain. We present extensive qualitative and quantitative results
for facial datasets and several different attributes that show the superiority
of our method. Additionally, our method is capable of adding, removing or
changing either fine-grained or coarse attributes by using an image as a
reference or by exploring the style distribution space, and it can be easily
extended to head-swapping and face-reenactment applications without being
trained on videos.
Related papers
- Leveraging Off-the-shelf Diffusion Model for Multi-attribute Fashion
Image Manipulation [27.587905673112473]
Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions.
Previous works typically employ conditional GANs where the generator explicitly learns the target attributes and directly execute the conversion.
We explore the classifier-guided diffusion that leverages the off-the-shelf diffusion model pretrained on general visual semantics such as Imagenet.
arXiv Detail & Related papers (2022-10-12T02:21:18Z) - Everything is There in Latent Space: Attribute Editing and Attribute
Style Manipulation by StyleGAN Latent Space Exploration [39.18239951479647]
We present Few-shot Latent-based Attribute Manipulation and Editing (FLAME)
FLAME is a framework to perform highly controlled image editing by latent space manipulation.
We generate diverse attribute styles in disentangled manner.
arXiv Detail & Related papers (2022-07-20T12:40:32Z) - Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with
Learned Morph Maps [94.10535575563092]
We introduce a generative adversarial network that can simultaneously generate aligned image samples from multiple related domains.
We propose Polymorphic-GAN which learns shared features across all domains and a per-domain morph layer to morph shared features according to each domain.
arXiv Detail & Related papers (2022-06-06T21:03:02Z) - Attribute Group Editing for Reliable Few-shot Image Generation [85.52840521454411]
We propose a new editing-based method, i.e., Attribute Group Editing (AGE), for few-shot image generation.
AGE examines the internal representation learned in GANs and identifies semantically meaningful directions.
arXiv Detail & Related papers (2022-03-16T06:54:09Z) - Disentangled Unsupervised Image Translation via Restricted Information
Flow [61.44666983942965]
Many state-of-art methods hard-code the desired shared-vs-specific split into their architecture.
We propose a new method that does not rely on inductive architectural biases.
We show that the proposed method achieves consistently high manipulation accuracy across two synthetic and one natural dataset.
arXiv Detail & Related papers (2021-11-26T00:27:54Z) - FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces [9.664892091493586]
FacialGAN is a novel framework enabling simultaneous rich style transfers and interactive facial attributes manipulation.
We show our model's capacity in producing visually compelling results in style transfer, attribute manipulation, diversity and face verification.
arXiv Detail & Related papers (2021-10-18T15:53:38Z) - Explaining in Style: Training a GAN to explain a classifier in
StyleSpace [75.75927763429745]
We present StylEx, a method for training a generative model to explain semantic attributes of an image.
StylEx finds attributes that align well with semantic ones, generate meaningful image-specific explanations, and are human-interpretable.
Our results show that the method finds attributes that align well with semantic ones, generate meaningful image-specific explanations, and are human-interpretable.
arXiv Detail & Related papers (2021-04-27T17:57:19Z) - TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation [82.52514546441247]
We propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style and the content.
We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.
arXiv Detail & Related papers (2020-04-19T05:07:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.