通过几何对齐特征与自回归机制，提升严重退化人脸的修复质量。
GeoMAR: Unleashing Geometrically Aligned Features for Masked Autoregressive Blind Face Restoration
- 双输入提取几何特征，用关键点对齐实现空间精准描述。
- 多步自回归生成，逐步优化面部复杂区域，提升结构一致性。
- 适用于真实场景下严重模糊或低质人脸修复，效果优于现有方法。
基于码本的盲人脸修复（BFR）在严重退化情况下常面临条件特征模糊和预测机制脆弱的问题。为此，本文提出GeoMAR框架，通过掩码自回归（MAR）精炼几何对齐特征，实现鲁棒的人脸修复。特征条件设计上，采用双输入提取管道，生成具有显式空间锚点的分量几何描述；通过对齐几何先验注入器（Aligned Geometric Priors Injector），利用KV-Q交换策略融合低质量（LQ）特征，生成几何对齐特征。预测机制方面，将单步映射重构为多步自回归过程，实现从粗到细的渐进式生成，基于日益可靠的上下文逐步优化复杂面部区域。在1个合成数据集和3个真实世界基准上的实验表明，GeoMAR在感知质量和视觉结构一致性方面均达到领先水平。代码已开源：https://github.com/BRL-SYSU/GeoMAR.git。
原文摘要 · Abstract (English)
Codebook-based blind face restoration (BFR) often suffers from ambiguous conditioning features and a fragile prediction mechanism under severe degradation. To address these challenges, we propose GeoMAR, a framework designed to unleash geometrically aligned features with masked autoregressive (MAR) refinement for robust face restoration. For feature conditioning, we introduce a dual-input extraction pipeline to extract component-based geometric descriptions with explicit, spatially faithful anchors. These textual priors are integrated with low-quality (LQ) features via an Aligned Geometric Priors Injector, which employs a KV-Q exchange strategy to generate geometrically aligned features. For prediction mechanism, we reformulate the one-step mapping into a multi-step MAR process. This coarse-to-fine generation progressively refines complex facial regions based on increasingly reliable context. Experiments on one synthetic and three real-world benchmarks demonstrate that GeoMAR achieves highly competitive perceptual quality and coherent visual structures compared with existing methods. The code is available at https://github.com/BRL-SYSU/GeoMAR.git.
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