Face Swap via Diffusion Model

• URL: http://arxiv.org/abs/2403.01108v2
• Date: Wed, 29 May 2024 13:12:40 GMT
• Title: Face Swap via Diffusion Model
• Authors: Feifei Wang,
• Abstract summary: This report presents a diffusion model based framework for face swapping between two portrait images. The basic framework consists of three components, for face feature encoding, multi-conditional generation, and face inpainting respectively.
• Score: 4.026688121914668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This technical report presents a diffusion model based framework for face swapping between two portrait images. The basic framework consists of three components, i.e., IP-Adapter, ControlNet, and Stable Diffusion's inpainting pipeline, for face feature encoding, multi-conditional generation, and face inpainting respectively. Besides, I introduce facial guidance optimization and CodeFormer based blending to further improve the generation quality. Specifically, we engage a recent light-weighted customization method (i.e., DreamBooth-LoRA), to guarantee the identity consistency by 1) using a rare identifier "sks" to represent the source identity, and 2) injecting the image features of source portrait into each cross-attention layer like the text features. Then I resort to the strong inpainting ability of Stable Diffusion, and utilize canny image and face detection annotation of the target portrait as the conditions, to guide ContorlNet's generation and align source portrait with the target portrait. To further correct face alignment, we add the facial guidance loss to optimize the text embedding during the sample generation. The code is available at: https://github.com/somuchtome/Faceswap

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)
• Fusion is all you need: Face Fusion for Customized Identity-Preserving Image Synthesis [7.099258248662009]
  Text-to-image (T2I) models have significantly advanced the development of artificial intelligence. However, existing T2I-based methods often struggle to accurately reproduce the appearance of individuals from a reference image. We leverage the pre-trained UNet from Stable Diffusion to incorporate the target face image directly into the generation process.
  arXiv  Detail & Related papers  (2024-09-27T19:31:04Z)
• FlashFace: Human Image Personalization with High-fidelity Identity Preservation [59.76645602354481]
  FlashFace allows users to easily personalize their own photos by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by higher-fidelity identity preservation and better instruction following.
  arXiv  Detail & Related papers  (2024-03-25T17:59:57Z)
• 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)
• Controllable 3D Face Generation with Conditional Style Code Diffusion [51.24656496304069]
  TEx-Face(TExt & Expression-to-Face) addresses challenges by dividing the task into three components, i.e., 3D GAN Inversion, Conditional Style Code Diffusion, and 3D Face Decoding. Experiments conducted on FFHQ, CelebA-HQ, and CelebA-Dialog demonstrate the promising performance of our TEx-Face.
  arXiv  Detail & Related papers  (2023-12-21T15:32:49Z)
• High-Fidelity Face Swapping with Style Blending [16.024260677867076]
  We propose an innovative end-to-end framework for high-fidelity face swapping. First, we introduce a StyleGAN-based facial attributes encoder that extracts essential features from faces and inverts them into a latent style code. Second, we introduce an attention-based style blending module to effectively transfer Face IDs from source to target.
  arXiv  Detail & Related papers  (2023-12-17T23:22:37Z)
• 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)
• 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)
• Face Transformer: Towards High Fidelity and Accurate Face Swapping [54.737909435708936]
  Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces. This paper presents Face Transformer, a novel face swapping network that can accurately preserve source identities and target attributes simultaneously.
  arXiv  Detail & Related papers  (2023-04-05T15:51:44Z)
• StyO: Stylize Your Face in Only One-Shot [8.253458555695767]
  This paper focuses on face stylization with a single artistic target. Existing works for this task often fail to retain the source content while achieving geometry variation. We present a novel StyO model, ie. Stylize the face in only One-shot, to solve the above problem.
  arXiv  Detail & Related papers  (2023-03-06T15:48:33Z)
• FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks [0.7043489166804575]
  We present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-to-image translation networks, that have successfully been used in the 2D domain, to 3D face geometry.
  arXiv  Detail & Related papers  (2021-12-01T14:42:03Z)

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.