We tackle a 3D scene stylization problem - generating stylized images of a
scene from arbitrary novel views given a set of images of the same scene and a
reference image of the desired style as inputs. Direct solution of combining
novel view synthesis and stylization approaches lead to results that are blurry
or not consistent across different views. We propose a point cloud-based method
for consistent 3D scene stylization. First, we construct the point cloud by
back-projecting the image features to the 3D space. Second, we develop point
cloud aggregation modules to gather the style information of the 3D scene, and
then modulate the features in the point cloud with a linear transformation
matrix. Finally, we project the transformed features to 2D space to obtain the
novel views. Experimental results on two diverse datasets of real-world scenes
validate that our method generates consistent stylized novel view synthesis
results against other alternative approaches.

本文提出一种基于点云的方法，通过反投影图像特征到 3D 空间、点云聚合模块获取 3D 场景的风格信息并用线性转换矩阵对点云特征进行变换、将变换后的特征投影到 2D 空间获得新视角来生成具有一致风格的 3D 场景，实验证明该方法相比其他方法生成的配景画面更加一致和美观。

学习新视角的风格化

Learning to Stylize Novel Views

Unsupervised cross-lingual word embedding (CLWE) methods learn a linear
transformation matrix that maps two monolingual embedding spaces that are
separately trained with monolingual corpora. This method relies on the
assumption that the two embedding spaces are structurally similar, which does
not necessarily hold true in general. In this paper, we argue that using a
pseudo-parallel corpus generated by an unsupervised machine translation model
facilitates the structural similarity of the two embedding spaces and improves
the quality of CLWEs in the unsupervised mapping method. We show that our
approach outperforms other alternative approaches given the same amount of
data, and, through detailed analysis, we show that data augmentation with the
pseudo data from unsupervised machine translation is especially effective for
mapping-based CLWEs because (1) the pseudo data makes the source and target
corpora (partially) parallel; (2) the pseudo data contains information on the
original language that helps to learn similar embedding spaces between the
source and target languages.

提出了一种使用非监督机器翻译生成的伪平行语料库以优化跨语言词嵌入的无监督映射方法的新思路，改进了现有方法，并通过详细分析证明伪数据增强方法特别适用于基于映射的跨语言词嵌入任务。