CodeFormer++: Blind Face Restoration Using Deformable Registration and Deep Metric Learning

• URL: http://arxiv.org/abs/2510.04410v1
• Date: Mon, 06 Oct 2025 00:53:50 GMT
• Title: CodeFormer++: Blind Face Restoration Using Deformable Registration and Deep Metric Learning
• Authors: Venkata Bharath Reddy Reddem, Akshay P Sarashetti, Ranjith Merugu, Amit Satish Unde,
• Abstract summary: We present CodeFormer++, a novel framework that maximizes the utility of generative priors for high-quality face restoration while preserving identity.<n>Our method makes three key contributions: (1) a learning-based deformable face registration module that semantically aligns generated and restored faces; (2) a texture guided restoration network to dynamically extract and transfer the texture of generated face to boost the quality of identity-preserving restored face; and (3) the integration of deep metric learning for BFR with the generation of informative positive and hard negative samples to better fuse identity- preserving and generative features.
• Score: 1.1666234644810893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Blind face restoration (BFR) has attracted increasing attention with the rise of generative methods. Most existing approaches integrate generative priors into the restoration pro- cess, aiming to jointly address facial detail generation and identity preservation. However, these methods often suffer from a trade-off between visual quality and identity fidelity, leading to either identity distortion or suboptimal degradation removal. In this paper, we present CodeFormer++, a novel framework that maximizes the utility of generative priors for high-quality face restoration while preserving identity. We decompose BFR into three sub-tasks: (i) identity- preserving face restoration, (ii) high-quality face generation, and (iii) dynamic fusion of identity features with realistic texture details. Our method makes three key contributions: (1) a learning-based deformable face registration module that semantically aligns generated and restored faces; (2) a texture guided restoration network to dynamically extract and transfer the texture of generated face to boost the quality of identity-preserving restored face; and (3) the integration of deep metric learning for BFR with the generation of informative positive and hard negative samples to better fuse identity- preserving and generative features. Extensive experiments on real-world and synthetic datasets demonstrate that, the pro- posed CodeFormer++ achieves superior performance in terms of both visual fidelity and identity consistency.

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