# PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency arXiv: https://arxiv.org/abs/2105.09180 ยท HTML: https://mlsanity.com/paper/2105.09180/ ## Key facts - Cited by 59 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 31 references that are in the map. - First posted on arXiv 19 May 2021. ## Builds on (most cited first, 9 of 9) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017) - [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020) - [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017) - [Conditional Sequential Modulation for Efficient Global Image Retouching](https://mlsanity.com/paper/2009.10390/) (2020) - [Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning](https://mlsanity.com/paper/1804.04450/) (2018) - [Aesthetic-Driven Image Enhancement by Adversarial Learning](https://mlsanity.com/paper/1707.05251/) (2017) - [Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software](https://mlsanity.com/paper/1912.07833/) (2019) ## Built on by (most cited first, 30 of 59) - [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024) - [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023) - [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022) - [IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation](https://mlsanity.com/paper/2403.10701/) (2024) - [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022) - [JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent](https://mlsanity.com/paper/2506.17612/) (2025) - [4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement](https://mlsanity.com/paper/2209.01749/) (2022) - [JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization](https://mlsanity.com/paper/2511.23002/) (2025) - [RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters](https://mlsanity.com/paper/2303.08682/) (2023) - [An Image Quality Assessment Dataset for Portraits](https://mlsanity.com/paper/2304.05772/) (2023) - [DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts](https://mlsanity.com/paper/2407.03757/) (2024) - [Physically Controllable Relighting of Photographs](https://mlsanity.com/paper/2508.05626/) (2025) - [Taming Lookup Tables for Efficient Image Retouching](https://mlsanity.com/paper/2403.19238/) (2024) - [InstructBrush: Learning Attention-based Instruction Optimization for Image Editing](https://mlsanity.com/paper/2403.18660/) (2024) - [CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment](https://mlsanity.com/paper/2404.01123/) (2024) - [MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills](https://mlsanity.com/paper/2505.06176/) (2025) - [Learning Diverse Tone Styles for Image Retouching](https://mlsanity.com/paper/2207.05430/) (2022) - [Neural Color Operators for Sequential Image Retouching](https://mlsanity.com/paper/2207.08080/) (2022) - [NamedCurves: Learned Image Enhancement via Color Naming](https://mlsanity.com/paper/2407.09892/) (2024) - [Visual Persona: Foundation Model for Full-Body Human Customization](https://mlsanity.com/paper/2503.15406/) (2025) - [DCT-Shield: A Robust Frequency Domain Defense against Malicious Image Editing](https://mlsanity.com/paper/2504.17894/) (2025) - [Personalized Image Enhancement Featuring Masked Style Modeling](https://mlsanity.com/paper/2306.09334/) (2023) - [PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching](https://mlsanity.com/paper/2511.12998/) (2025) - [Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography](https://mlsanity.com/paper/2407.17996/) (2024) - [Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid](https://mlsanity.com/paper/2112.04107/) (2021) - [Generalized Lightness Adaptation with Channel Selective Normalization](https://mlsanity.com/paper/2308.13783/) (2023) - [Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors](https://mlsanity.com/paper/2409.18899/) (2024) - [Learning Adaptive Lighting via Channel-Aware Guidance](https://mlsanity.com/paper/2412.01493/) (2024) - [Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2503.11781/) (2025) - [Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables](https://mlsanity.com/paper/2508.16121/) (2025) ## Abstract (arXiv) Different from general photo retouching tasks, portrait photo retouching (PPR), which aims to enhance the visual quality of a collection of flat-looking portrait photos, has its special and practical requirements such as human-region priority (HRP) and group-level consistency (GLC). HRP requires that more attention should be paid to human regions, while GLC requires that a group of portrait photos should be retouched to a consistent tone. Models trained on existing general photo retouching datasets, however, can hardly meet these requirements of PPR. To facilitate the research on this high-frequency task, we construct a large-scale PPR dataset, namely PPR10K, which is the first of its kind to our best knowledge. PPR10K contains 1, 681 groups and 11, 161 high-quality raw portrait photos in total. High-resolution segmentation masks of human regions are provided. Each raw photo is retouched by three experts, while they elaborately adjust each group of photos to have consistent tones. We define a set of objective measures to evaluate the performance of PPR and propose strategies to learn PPR models with good HRP and GLC performance. The constructed PPR10K dataset provides a good benchmark for studying automatic PPR methods, and experiments demonstrate that the proposed learning strategies are effective to improve the retouching performance. Datasets and codes are available: https://github.com/csjliang/PPR10K.