EFHQ: Multi-purpose ExtremePose-Face-HQ dataset

• URL: http://arxiv.org/abs/2312.17205v4
• Date: Thu, 11 Apr 2024 18:48:04 GMT
• Title: EFHQ: Multi-purpose ExtremePose-Face-HQ dataset
• Authors: Trung Tuan Dao, Duc Hong Vu, Cuong Pham, Anh Tran,
• Abstract summary: This work introduces a novel dataset named Extreme Pose Face High-Quality dataset (EFHQ), which includes a maximum of 450k high-quality images of faces at extreme poses. To produce such a massive dataset, we utilize a novel and meticulous dataset processing pipeline to curate two publicly available datasets. Our dataset can complement existing datasets on various facial-related tasks, such as facial synthesis with 2D/3D-aware GAN, diffusion-based text-to-image face generation, and face reenactment.
• Score: 1.8194090162317431
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The existing facial datasets, while having plentiful images at near frontal views, lack images with extreme head poses, leading to the downgraded performance of deep learning models when dealing with profile or pitched faces. This work aims to address this gap by introducing a novel dataset named Extreme Pose Face High-Quality Dataset (EFHQ), which includes a maximum of 450k high-quality images of faces at extreme poses. To produce such a massive dataset, we utilize a novel and meticulous dataset processing pipeline to curate two publicly available datasets, VFHQ and CelebV-HQ, which contain many high-resolution face videos captured in various settings. Our dataset can complement existing datasets on various facial-related tasks, such as facial synthesis with 2D/3D-aware GAN, diffusion-based text-to-image face generation, and face reenactment. Specifically, training with EFHQ helps models generalize well across diverse poses, significantly improving performance in scenarios involving extreme views, confirmed by extensive experiments. Additionally, we utilize EFHQ to define a challenging cross-view face verification benchmark, in which the performance of SOTA face recognition models drops 5-37% compared to frontal-to-frontal scenarios, aiming to stimulate studies on face recognition under severe pose conditions in the wild.

Related papers

• AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior [13.27748226506837]
  Blind face restoration (BFR) is a fundamental and challenging problem in computer vision. Recent research endeavors rely on facial image priors from the powerful pretrained text-to-image (T2I) diffusion models. We propose AuthFace, which achieves highly authentic face restoration results by exploring a face-oriented generative diffusion prior.
  arXiv  Detail & Related papers  (2024-10-13T14:56:13Z)
• DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis [71.40724659748787]
  DiffusionFace is the first diffusion-based face forgery dataset. It covers various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms. It provides essential metadata and a real-world internet-sourced forgery facial image dataset for evaluation.
  arXiv  Detail & Related papers  (2024-03-27T11:32:44Z)
• Arc2Face: A Foundation Model for ID-Consistent Human Faces [95.00331107591859]
  Arc2Face is an identity-conditioned face foundation model. It can generate diverse photo-realistic images with an unparalleled degree of face similarity than existing models.
  arXiv  Detail & Related papers  (2024-03-18T10:32:51Z)
• Multiface: A Dataset for Neural Face Rendering [108.44505415073579]
  In this work, we present Multiface, a new multi-view, high-resolution human face dataset. We introduce Mugsy, a large scale multi-camera apparatus to capture high-resolution synchronized videos of a facial performance. The goal of Multiface is to close the gap in accessibility to high quality data in the academic community and to enable research in VR telepresence.
  arXiv  Detail & Related papers  (2022-07-22T17:55:39Z)
• Latents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images [29.496302682744133]
  We do away with the priors and complex pre-processing operations required by SOTA multi-class face segmentation models. We present results for our model's performance on the CelebAMask-HQ and HELEN datasets.
  arXiv  Detail & Related papers  (2022-07-05T08:09:15Z)
• Joint Face Image Restoration and Frontalization for Recognition [79.78729632975744]
  In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. Previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. We propose an Multi-Degradation Face Restoration model to restore frontalized high-quality faces from the given low-quality ones.
  arXiv  Detail & Related papers  (2021-05-12T03:52:41Z)
• SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis [65.35922024067551]
  We propose a generative adversarial network (GAN) -based model to generate high-quality, identity preserving frontal faces. Specifically, we propose SuperFront-GAN to synthesize a high-resolution (HR), frontal face from one-to-many LR faces with various poses. We integrate a super-resolution side-view module into SF-GAN to preserve identity information and fine details of the side-views in HR space.
  arXiv  Detail & Related papers  (2020-12-07T23:30:28Z)
• 3D-Aided Data Augmentation for Robust Face Understanding [40.73929372872909]
  We propose a method that produces realistic 3D augmented images from multiple viewpoints with different illumination conditions through 3D face modeling. Experiments demonstrate that the proposed 3D data augmentation method significantly improves the performance and robustness of various face understanding tasks.
  arXiv  Detail & Related papers  (2020-10-03T01:18:07Z)
• Methodology for Building Synthetic Datasets with Virtual Humans [1.5556923898855324]
  Large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities.
  arXiv  Detail & Related papers  (2020-06-21T10:29:36Z)
• DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition [94.96686189033869]
  We propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) DotFAN can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets collected from other domains. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity.
  arXiv  Detail & Related papers  (2020-02-23T08:16:34Z)

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.