Artistic style transfer aims at migrating the style from an example image to
a content image. Currently, optimization-based methods have achieved great
stylization quality, but expensive time cost restricts their practical
applications. Meanwhile, feed-forward methods still fail to synthesize complex
style, especially when holistic global and local patterns exist. Inspired by
the common painting process of drawing a draft and revising the details, we
introduce a novel feed-forward method named Laplacian Pyramid Network
(LapStyle). LapStyle first transfers global style patterns in low-resolution
via a Drafting Network. It then revises the local details in high-resolution
via a Revision Network, which hallucinates a residual image according to the
draft and the image textures extracted by Laplacian filtering. Higher
resolution details can be easily generated by stacking Revision Networks with
multiple Laplacian pyramid levels. The final stylized image is obtained by
aggregating outputs of all pyramid levels. %We also introduce a patch
discriminator to better learn local patterns adversarially. Experiments
demonstrate that our method can synthesize high quality stylized images in real
time, where holistic style patterns are properly transferred.

介绍了一种名为 LapStyle 的新型前馈方法，使用分层拉普拉斯金字塔网络来迁移全局风格模式并修正局部细节，同时添加了一个修补判别器以更好地学习局部模式，实验证明我们的方法能够以实时生成高质量的艺术风格图像。

用于快速高质量的艺术风格转换的拉普拉斯金字塔网络的草稿和修订

Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality  Artistic Style Transfer

Point clouds are often sparse and incomplete. Existing shape completion
methods are incapable of generating details of objects or learning the complex
point distributions. To this end, we propose a cascaded refinement network
together with a coarse-to-fine strategy to synthesize the detailed object
shapes. Considering the local details of partial input with the global shape
information together, we can preserve the existing details in the incomplete
point set and generate the missing parts with high fidelity. We also design a
patch discriminator that guarantees every local area has the same pattern with
the ground truth to learn the complicated point distribution. Quantitative and
qualitative experiments on different datasets show that our method achieves
superior results compared to existing state-of-the-art approaches on the 3D
point cloud completion task. Our source code is available at
this https URL

提出了一种级联细化网络结合粗到细的策略，通过考虑局部输入的细节和全局形状信息，保留了不完整点集中的现有细节，生成高保真度的缺失部分，并设计了一个补丁鉴别器来学习复杂的点分布，实验结果表明在 3D 点云完成任务中，我们的方法优于现有最先进的方法。