{"ID":2859642,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04410","arxiv_id":"2510.04410","title":"CodeFormer++: Blind Face Restoration Using Deformable Registration and Deep Metric Learning","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.","short_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 bet...","url_abs":"https://arxiv.org/abs/2510.04410","url_pdf":"https://arxiv.org/pdf/2510.04410v1","authors":"[\"Venkata Bharath Reddy Reddem\",\"Akshay P Sarashetti\",\"Ranjith Merugu\",\"Amit Satish Unde\"]","published":"2025-10-06T00:53:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}