MirrorPPR: Exemplar-Based Portrait Photo Retouching

While text-guided image editing has made remarkable progress,it remains limited in structural portrait retouching. Textual descriptionsstruggle to convey fine-grained changes to facial features and body pro-portions. To address this gap, we introduce Exemplar-Based PortraitPhoto Retouching, where the model is given an exemplar pair and taskedwith inferring and applying the same retouching operations to a newquery image. Existing exemplar-based editing methods primarily focuson tasks with pronounced visual transformations. In contrast, structuralportrait retouching involves extremely delicate and localized modifica-tions, making accurate extraction and transfer of these edits challeng-ing. To tackle this, we propose MirrorPPR, a novel framework specifi-cally designed to capture and transfer subtle structural retouching oper-ations. Our method uses a Retouching Operation Extractor to capturethe subtle differences from the exemplar pair. The extracted represen-tations are then injected into a pre-trained Diffusion Transformer (DiT)through a connector and Low-Rank Adaptation (LoRA) modules. Fur-thermore, constructing perfectly aligned cross-identity training pairs isseverely hindered by operation misalignment. To overcome this, we pro-pose an advanced data self-augmentation paradigm that ensures strictlyaligned retouching operations. To alleviate data scarcity and supportthis novel task, we introduce MirrorPPR47M, a large-scale dataset withover 47 million retouched pairs. By structuring the dataset into simu-lated and professional subsets, we enable progressive curriculum learningto smoothly optimize the network. Extensive experiments demonstratethat MirrorPPR significantly outperforms existing baselines in both re-touching quality and identity preservation. The project page is availableat https://sjtu-deng-lab.github.io/MirrorPPR.