Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2

• URL: http://arxiv.org/abs/2210.00586v2
• Date: Mon, 5 Jun 2023 20:25:22 GMT
• Title: Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2
• Authors: Ali Borji
• Abstract summary: We conduct a comparison of three popular systems including Stable Diffusion, Midjourney, and DALL-E 2 in their ability to generate photorealistic faces in the wild. We find that Stable Diffusion generates better faces than the other systems, according to the FID score. We also introduce a dataset of generated faces in the wild dubbed GFW, including a total of 15,076 faces.
• Score: 47.64219291655723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of image synthesis has made great strides in the last couple of years. Recent models are capable of generating images with astonishing quality. Fine-grained evaluation of these models on some interesting categories such as faces is still missing. Here, we conduct a quantitative comparison of three popular systems including Stable Diffusion, Midjourney, and DALL-E 2 in their ability to generate photorealistic faces in the wild. We find that Stable Diffusion generates better faces than the other systems, according to the FID score. We also introduce a dataset of generated faces in the wild dubbed GFW, including a total of 15,076 faces. Furthermore, we hope that our study spurs follow-up research in assessing the generative models and improving them. Data and code are available at data and code, respectively.

Related papers

• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration. We propose OSDFace, a novel one-step diffusion model for face restoration. Results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics.
  arXiv  Detail & Related papers  (2024-11-26T07:07:48Z)
• TCDiff: Triple Condition Diffusion Model with 3D Constraints for Stylizing Synthetic Faces [1.7535229154829601]
  Face recognition experiments using 1k, 2k, and 5k classes of our new dataset for training outperform state-of-the-art synthetic datasets in real face benchmarks.
  arXiv  Detail & Related papers  (2024-09-05T14:59:41Z)
• Comparative Analysis of Generative Models: Enhancing Image Synthesis with VAEs, GANs, and Stable Diffusion [0.0]
  This paper examines three major generative modelling frameworks: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs) and Stable Diffusion models.
  arXiv  Detail & Related papers  (2024-08-16T13:50:50Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Stable Diffusion for Data Augmentation in COCO and Weed Datasets [5.81198182644659]
  This study utilized seven common categories and three widespread weed species to evaluate the efficiency of a stable diffusion model. Three techniques (i.e., Image-to-image translation, Dreambooth, and ControlNet) based on stable diffusion were leveraged for image generation with different focuses. Then, classification and detection tasks were conducted based on these synthetic images, whose performance was compared to the models trained on original images.
  arXiv  Detail & Related papers  (2023-12-07T02:23:32Z)
• DCFace: Synthetic Face Generation with Dual Condition Diffusion Model [18.662943303044315]
  We propose a Dual Condition Face Generator (DCFace) based on a diffusion model. Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.
  arXiv  Detail & Related papers  (2023-04-14T11:31:49Z)
• NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-shot Real Image Animation [66.0838349951456]
  Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. We propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image.
  arXiv  Detail & Related papers  (2022-11-30T18:36:45Z)
• Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution [68.8204255655161]
  A master face is a face image that passes face-based identity authentication for a high percentage of the population. We optimize these faces for 2D and 3D face verification models. In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network.
  arXiv  Detail & Related papers  (2022-11-25T09:15:38Z)
• Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection [0.0]
  We use GAN-based data augmentation to generate extra dataset instances. We validated our work by evaluating the accuracy of fine-grained image classification on the recent Vision Transformer (ViT) Model.
  arXiv  Detail & Related papers  (2021-08-28T06:32:42Z)
• OSTeC: One-Shot Texture Completion [86.23018402732748]
  We propose an unsupervised approach for one-shot 3D facial texture completion. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator. We frontalize the target image by projecting the completed texture into the generator.
  arXiv  Detail & Related papers  (2020-12-30T23:53:26Z)

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.