Makeup transfer is the task of applying on a source face the makeup style
from a reference image. Real-life makeups are diverse and wild, which cover not
only color-changing but also patterns, such as stickers, blushes, and
jewelries. However, existing works overlooked the latter components and
confined makeup transfer to color manipulation, focusing only on light makeup
styles. In this work, we propose a holistic makeup transfer framework that can
handle all the mentioned makeup components. It consists of an improved color
transfer branch and a novel pattern transfer branch to learn all makeup
properties, including color, shape, texture, and location. To train and
evaluate such a system, we also introduce new makeup datasets for real and
synthetic extreme makeup. Experimental results show that our framework achieves
the state of the art performance on both light and extreme makeup styles. Code
is available at this https URL

这项工作介绍了一个全面的化妆迁移框架，可以处理妆容的所有元素，并引入了新的真实和合成极限妆容数据集进行系统的训练和评估。实验结果表明，该框架在轻妆和极限妆上均取得了最先进的性能。

口红不足以为：野外妆容迁移的去色配对技术

Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup  Transfer

Arbitrary text appearance poses a great challenge in scene text recognition
tasks. Existing works mostly handle with the problem in consideration of the
shape distortion, including perspective distortions, line curvature or other
style variations. Therefore, methods based on spatial transformers are
extensively studied. However, chromatic difficulties in complex scenes have not
been paid much attention on. In this work, we introduce a new learnable
geometric-unrelated module, the Structure-Preserving Inner Offset Network
(SPIN), which allows the color manipulation of source data within the network.
This differentiable module can be inserted before any recognition architecture
to ease the downstream tasks, giving neural networks the ability to actively
transform input intensity rather than the existing spatial rectification. It
can also serve as a complementary module to known spatial transformations and
work in both independent and collaborative ways with them. Extensive
experiments show that the use of SPIN results in a significant improvement on
multiple text recognition benchmarks compared to the state-of-the-arts.

本研究介绍了一种可学习的几何无关模块 ——SPIN，该模块允许神经网络主动变换输入强度颜色，以解决场景文字识别任务中的色彩困难问题，并与现有的空间变换模块协同工作，实验结果表明，使用 SPIN 可以显著提高多个文本识别基准的性能。