Visual content creation has spurred a soaring interest given its applications
in mobile photography and AR / VR. Style transfer and single-image 3D
photography as two representative tasks have so far evolved independently. In
this paper, we make a connection between the two, and address the challenging
task of 3D photo stylization - generating stylized novel views from a single
image given an arbitrary style. Our key intuition is that style transfer and
view synthesis have to be jointly modeled for this task. To this end, we
propose a deep model that learns geometry-aware content features for
stylization from a point cloud representation of the scene, resulting in
high-quality stylized images that are consistent across views. Further, we
introduce a novel training protocol to enable the learning using only 2D
images. We demonstrate the superiority of our method via extensive qualitative
and quantitative studies, and showcase key applications of our method in light
of the growing demand for 3D content creation from 2D image assets.

本文提出了一个深度模型，在场景的点云表示中学习基于几何感知的内容特征，以生成高质量且在视图上具有一致性的艺术化图像，从而实现了从单个图像到任意风格化图像的 3D 照片艺术化生成，并在定性和定量研究中展示了方法的优越性。