Identity and Attribute Preserving Thumbnail Upscaling
- URL: http://arxiv.org/abs/2105.14609v1
- Date: Sun, 30 May 2021 19:32:27 GMT
- Title: Identity and Attribute Preserving Thumbnail Upscaling
- Authors: Noam Gat, Sagie Benaim, Lior Wolf
- Abstract summary: We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person's identity and other attributes.
Our results indicate an improvement in face similarity recognition and lookalike generation as well as in the ability to generate higher resolution images which preserve an input thumbnail identity and whose race and attributes are maintained.
- Score: 93.38607559281601
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the task of upscaling a low resolution thumbnail image of a
person, to a higher resolution image, which preserves the person's identity and
other attributes. Since the thumbnail image is of low resolution, many higher
resolution versions exist. Previous approaches produce solutions where the
person's identity is not preserved, or biased solutions, such as predominantly
Caucasian faces. We address the existing ambiguity by first augmenting the
feature extractor to better capture facial identity, facial attributes (such as
smiling or not) and race, and second, use this feature extractor to generate
high-resolution images which are identity preserving as well as conditioned on
race and facial attributes. Our results indicate an improvement in face
similarity recognition and lookalike generation as well as in the ability to
generate higher resolution images which preserve an input thumbnail identity
and whose race and attributes are maintained.
Related papers
- Adversarial Identity Injection for Semantic Face Image Synthesis [6.763801424109435]
We present an SIS architecture that exploits a cross-attention mechanism to merge identity, style, and semantic features to generate faces.
Experimental results reveal that the proposed method is not only suitable for preserving the identity but is also effective in the face recognition adversarial attack.
arXiv Detail & Related papers (2024-04-16T09:19:23Z) - When StyleGAN Meets Stable Diffusion: a $\mathscr{W}_+$ Adapter for
Personalized Image Generation [60.305112612629465]
Text-to-image diffusion models have excelled in producing diverse, high-quality, and photo-realistic images.
We present a novel use of the extended StyleGAN embedding space $mathcalW_+$ to achieve enhanced identity preservation and disentanglement for diffusion models.
Our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions.
arXiv Detail & Related papers (2023-11-29T09:05:14Z) - DreamIdentity: Improved Editability for Efficient Face-identity
Preserved Image Generation [69.16517915592063]
We propose a novel face-identity encoder to learn an accurate representation of human faces.
We also propose self-augmented editability learning to enhance the editability of models.
Our methods can generate identity-preserved images under different scenes at a much faster speed.
arXiv Detail & Related papers (2023-07-01T11:01:17Z) - Attribute-preserving Face Dataset Anonymization via Latent Code
Optimization [64.4569739006591]
We present a task-agnostic anonymization procedure that directly optimize the images' latent representation in the latent space of a pre-trained GAN.
We demonstrate through a series of experiments that our method is capable of anonymizing the identity of the images whilst -- crucially -- better-preserving the facial attributes.
arXiv Detail & Related papers (2023-03-20T17:34:05Z) - StyleID: Identity Disentanglement for Anonymizing Faces [4.048444203617942]
The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID.
As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement.
StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.
arXiv Detail & Related papers (2022-12-28T12:04:24Z) - FICGAN: Facial Identity Controllable GAN for De-identification [34.38379234653657]
We present Facial Identity Controllable GAN (FICGAN) for generating high-quality de-identified face images with ensured privacy protection.
Based on the analysis, we develop FICGAN, an autoencoder-based conditional generative model that learns to disentangle the identity attributes from non-identity attributes on a face image.
arXiv Detail & Related papers (2021-10-02T07:09:27Z) - Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails [53.080403912727604]
We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN.
It exploits facial geometry priors to replenish and upsample (8*) the occluded and tiny faces.
Pro-UIGAN achieves visually pleasing HR faces, reaching superior performance in downstream tasks.
arXiv Detail & Related papers (2021-08-02T02:29:24Z) - Face Anonymization by Manipulating Decoupled Identity Representation [5.26916168336451]
We propose a novel approach which protects identity information of facial images from leakage with slightest modification.
Specifically, we disentangle identity representation from other facial attributes leveraging the power of generative adversarial networks.
We evaulate the disentangle ability of our model, and propose an effective method for identity anonymization, namely Anonymous Identity Generation (AIG)
arXiv Detail & Related papers (2021-05-24T07:39:54Z) - Attributes Aware Face Generation with Generative Adversarial Networks [133.44359317633686]
We propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN.
Three stacked generators generate $64 times 64$, $128 times 128$ and $256 times 256$ resolution face images respectively.
In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes.
arXiv Detail & Related papers (2020-12-03T09:25:50Z) - VAE/WGAN-Based Image Representation Learning For Pose-Preserving
Seamless Identity Replacement In Facial Images [15.855376604558977]
We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss.
We show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing.
arXiv Detail & Related papers (2020-03-02T03:35:59Z)
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.