HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model

• URL: http://arxiv.org/abs/2505.18469v1
• Date: Sat, 24 May 2025 02:19:20 GMT
• Title: HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model
• Authors: Jingkai Wang, Wu Miao, Jue Gong, Zheng Chen, Xing Liu, Hong Gu, Yutong Liu, Yulun Zhang,
• Abstract summary: HonestFace is a novel approach designed to restore faces with a strong emphasis on such honesty.<n>Masked face alignment method is presented to enhance fine-grained details and textural authenticity.<n>Our approach surpasses existing state-of-the-art methods, achieving superior performance in both visual quality and quantitative assessments.
• Score: 36.36629793211904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face restoration has achieved remarkable advancements through the years of development. However, ensuring that restored facial images exhibit high fidelity, preserve authentic features, and avoid introducing artifacts or biases remains a significant challenge. This highlights the need for models that are more "honest" in their reconstruction from low-quality inputs, accurately reflecting original characteristics. In this work, we propose HonestFace, a novel approach designed to restore faces with a strong emphasis on such honesty, particularly concerning identity consistency and texture realism. To achieve this, HonestFace incorporates several key components. First, we propose an identity embedder to effectively capture and preserve crucial identity features from both the low-quality input and multiple reference faces. Second, a masked face alignment method is presented to enhance fine-grained details and textural authenticity, thereby preventing the generation of patterned or overly synthetic textures and improving overall clarity. Furthermore, we present a new landmark-based evaluation metric. Based on affine transformation principles, this metric improves the accuracy compared to conventional L2 distance calculations for facial feature alignment. Leveraging these contributions within a one-step diffusion model framework, HonestFace delivers exceptional restoration results in terms of facial fidelity and realism. Extensive experiments demonstrate that our approach surpasses existing state-of-the-art methods, achieving superior performance in both visual quality and quantitative assessments. The code and pre-trained models will be made publicly available at https://github.com/jkwang28/HonestFace .

Related papers

• Reference-Guided Identity Preserving Face Restoration [54.10295747851343]
  Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration.<n>This paper introduces a novel approach that maximizes reference face utility for improved face restoration and identity preservation.
  arXiv  Detail & Related papers  (2025-05-28T02:46:34Z)
• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration.<n>We propose OSDFace, a novel one-step diffusion model for face restoration.<n>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)
• 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)
• G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors [71.69161292330504]
  Reversible face anonymization seeks to replace sensitive identity information in facial images with synthesized alternatives. This paper introduces Gtextsuperscript2Face, which leverages both generative and geometric priors to enhance identity manipulation. Our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility.
  arXiv  Detail & Related papers  (2024-08-18T12:36:47Z)
• CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models [57.9771859175664]
  Recent generative-prior-based methods have shown promising blind face restoration performance. Generating fine-grained facial details faithful to inputs remains a challenging problem. We introduce a diffusion-based-prior inside a VQGAN architecture that focuses on learning the distribution over uncorrupted latent embeddings.
  arXiv  Detail & Related papers  (2024-02-08T23:51:49Z)
• 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)
• Effective Adapter for Face Recognition in the Wild [72.75516495170199]
  We tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional approaches-either training models directly on degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective. We propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets.
  arXiv  Detail & Related papers  (2023-12-04T08:55:46Z)
• Fine-grained Identity Preserving Landmark Synthesis for Face Reenactment [30.062379710262068]
  A landmark synthesis network is designed to generate fine-grained landmark faces with more details. The network refines the manipulated landmarks and generates a smooth and gradually changing face landmark sequence with good identity preserving ability. Experiments are conducted on our self-collected BeautySelfie and the public VoxCeleb1 datasets.
  arXiv  Detail & Related papers  (2021-10-10T05:25:23Z)
• 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)

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.