# MirrorPPR：基于示例的人像照片修图

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-28 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmr17wpih00dcslnl8nci6p0n
- 原文链接：https://arxiv.org/abs/2606.29308

## AI 摘要

MirrorPPR 提出基于示例的结构化人像修图方法，通过 Retouching Operation Extractor 从示例对中提取细微修图操作，经连接器和 LoRA 模块注入预训练的 Diffusion Transformer（DiT）。为克服跨身份训练中的操作对齐难题，设计数据自增强范式确保严格对齐，并构建含超 4700 万对修图样本的大规模数据集 MirrorPPR47M，按模拟与专业子集组织以支持渐进课程学习。实验表明 MirrorPPR 在修图质量和身份保留上显著优于现有基线。

## 正文

While text-guided image editing has made remarkable progress, it remains limited in structural portrait retouching. Textual descriptions struggle to convey fine-grained changes to facial features and body proportions. To address this gap, we introduce Exemplar-Based Portrait Photo Retouching, where the model is given an exemplar pair and tasked with inferring and applying the same retouching operations to a new query image. Existing exemplar-based editing methods primarily focus on tasks with pronounced visual transformations. In contrast, structural portrait retouching involves extremely delicate and localized modifications, making accurate extraction and transfer of these edits challenging. To tackle this, we propose MirrorPPR, a novel framework designed to capture and transfer subtle structural retouching operations. Our method uses a Retouching Operation Extractor to capture the subtle differences from the exemplar pair. The extracted representations are then injected into a pre-trained Diffusion Transformer (DiT) through a connector and Low-Rank Adaptation (LoRA) modules. Furthermore, constructing perfectly aligned cross-identity training pairs is severely hindered by operation misalignment. To overcome this, we propose an advanced data self-augmentation paradigm that ensures strictly aligned retouching operations. To alleviate data scarcity and support this novel task, we introduce MirrorPPR47M, a large-scale dataset with over 47 million retouched pairs. By structuring the dataset into simulated and professional subsets, we enable progressive curriculum learning to smoothly optimize the network. Extensive experiments demonstrate that MirrorPPR significantly outperforms existing baselines in both retouching quality and identity preservation. The project page is available at https://sjtu-deng-lab.github.io/MirrorPPR.