# FaceMe: Robust Blind Face Restoration with Personal Identification

arXiv: https://arxiv.org/abs/2501.05177 · HTML: https://mlsanity.com/paper/2501.05177/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- First posted on arXiv 9 Jan 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Built on by (most cited first, 14 of 14)

- [The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results](https://mlsanity.com/paper/2604.10532/) (2026)
- [Improving Reconstruction of Representation Autoencoder](https://mlsanity.com/paper/2602.08620/) (2026)
- [Trust but Verify: Adaptive Conditioning for Reference-Based Diffusion Super-Resolution via Implicit Reference Correlation Modeling](https://mlsanity.com/paper/2602.01864/) (2026)
- [Robust ID-Specific Face Restoration via Alignment Learning](https://mlsanity.com/paper/2507.10943/) (2025)
- [HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model](https://mlsanity.com/paper/2505.18469/) (2025)
- [RefSTAR: Blind Facial Image Restoration with Reference Selection, Transfer, and Reconstruction](https://mlsanity.com/paper/2507.10470/) (2025)
- [LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter](https://mlsanity.com/paper/2505.23462/) (2025)
- [Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration](https://mlsanity.com/paper/2603.16570/) (2026)
- [MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration](https://mlsanity.com/paper/2603.18645/) (2026)
- [TimeWeaver: Age-Consistent Reference-Based Face Restoration with Identity Preservation](https://mlsanity.com/paper/2603.22701/) (2026)
- [IConFace: Fine-Grained Identity Conditioning for Reference-Aware Face Restoration](https://mlsanity.com/paper/2605.02814/) (2026)
- [FADRA: Frequency-Aware Diffusion with Residual Adaptation for Video Face Restoration](https://mlsanity.com/paper/2607.06389/) (2026)
- [Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap](https://mlsanity.com/paper/2608.06580/) (2026)
- [Beyond Lip Sync: Reference-Grounded Oral Refinement for Audio-Driven Portrait Animation](https://mlsanity.com/paper/2609.38019/) (2026)

## Abstract (arXiv)

Blind face restoration is a highly ill-posed problem due to the lack of necessary context. Although existing methods produce high-quality outputs, they often fail to faithfully preserve the individual's identity. In this paper, we propose a personalized face restoration method, FaceMe, based on a diffusion model. Given a single or a few reference images, we use an identity encoder to extract identity-related features, which serve as prompts to guide the diffusion model in restoring high-quality and identity-consistent facial images. By simply combining identity-related features, we effectively minimize the impact of identity-irrelevant features during training and support any number of reference image inputs during inference. Additionally, thanks to the robustness of the identity encoder, synthesized images can be used as reference images during training, and identity changing during inference does not require fine-tuning the model. We also propose a pipeline for constructing a reference image training pool that simulates the poses and expressions that may appear in real-world scenarios. Experimental results demonstrate that our FaceMe can restore high-quality facial images while maintaining identity consistency, achieving excellent performance and robustness.