  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.
