  Portrait photo retouching is a photo retouching task that emphasizes
human-region priority and group-level consistency. The lookup table-based
method achieves promising retouching performance by learning image-adaptive
weights to combine 3-dimensional lookup tables (3D LUTs) and conducting
pixel-to-pixel color transformation. However, this paradigm ignores the local
context cues and applies the same transformation to portrait pixels and
background pixels when they exhibit the same raw RGB values. In contrast, an
expert usually conducts different operations to adjust the color temperatures
and tones of portrait regions and background regions. This inspires us to model
local context cues to improve the retouching quality explicitly. Firstly, we
consider an image patch and predict pixel-adaptive lookup table weights to
precisely retouch the center pixel. Secondly, as neighboring pixels exhibit
different affinities to the center pixel, we estimate a local attention mask to
modulate the influence of neighboring pixels. Thirdly, the quality of the local
attention mask can be further improved by applying supervision, which is based
on the affinity map calculated by the groundtruth portrait mask. As for
group-level consistency, we propose to directly constrain the variance of mean
color components in the Lab space. Extensive experiments on PPR10K dataset
verify the effectiveness of our method, e.g. on high-resolution photos, the
PSNR metric receives over 0.5 gains while the group-level consistency metric
obtains at least 2.1 decreases.
