Modern 3D-GANs synthesize geometry and texture by training on large-scale
datasets with a consistent structure. Training such models on stylized,
artistic data, with often unknown, highly variable geometry, and camera
information has not yet been shown possible. Can we train a 3D GAN on such
artistic data, while maintaining multi-view consistency and texture quality? To
this end, we propose an adaptation framework, where the source domain is a
pre-trained 3D-GAN, while the target domain is a 2D-GAN trained on artistic
datasets. We then distill the knowledge from a 2D generator to the source 3D
generator. To do that, we first propose an optimization-based method to align
the distributions of camera parameters across domains. Second, we propose
regularizations necessary to learn high-quality texture, while avoiding
degenerate geometric solutions, such as flat shapes. Third, we show a
deformation-based technique for modeling exaggerated geometry of artistic
domains, enabling -- as a byproduct -- personalized geometric editing. Finally,
we propose a novel inversion method for 3D-GANs linking the latent spaces of
the source and the target domains. Our contributions -- for the first time --
allow for the generation, editing, and animation of personalized artistic 3D
avatars on artistic datasets.

本文提出了一种适应性框架，它能够使 3D-GAN 在艺术数据上训练，同时保持多视角的一致性和良好的纹理质量，通过优化方法对摄像机参数进行分布对齐，提出了纹理常规化技术，同时避免退化几何解，并展示了一种变形技术，使艺术领域的几何形态更加夸张，可以个性化的进行几何编辑，最终实现了在艺术数据集上生成、编辑和动画化个性化的 3D 角色。

3DAvatarGAN: 搭建领域以获得可个性化编辑的头像

3DAvatarGAN: Bridging Domains for Personalized Editable Avatars

The applicability of computer vision to real paintings and artworks has been
rarely investigated, even though a vast heritage would greatly benefit from
techniques which can understand and process data from the artistic domain. This
is partially due to the small amount of annotated artistic data, which is not
even comparable to that of natural images captured by cameras. In this paper,
we propose a semantic-aware architecture which can translate artworks to
photo-realistic visualizations, thus reducing the gap between visual features
of artistic and realistic data. Our architecture can generate natural images by
retrieving and learning details from real photos through a similarity matching
strategy which leverages a weakly-supervised semantic understanding of the
scene. Experimental results show that the proposed technique leads to increased
realism and to a reduction in domain shift, which improves the performance of
pre-trained architectures for classification, detection, and segmentation. Code
is publicly available at: this https URL

通过语义感知的计算机视觉技术，将艺术作品转化为照片般写实的图像，降低了艺术数据与真实数据的视觉差距，并取得了分类、检测和分割任务的表现优化。

Art2Real：通过语义感知的图像翻译展示艺术品的真实面貌

Art2Real: Unfolding the Reality of Artworks via Semantically-Aware  Image-to-Image Translation

Vast amounts of artistic data is scattered on-line from both museums and art
applications. Collecting, processing and studying it with respect to all
accompanying attributes is an expensive process. With a motivation to speed up
and improve the quality of categorical analysis in the artistic domain, in this
paper we propose an efficient and accurate method for multi-task learning with
a shared representation applied in the artistic domain. We continue to show how
different multi-task configurations of our method behave on artistic data and
outperform handcrafted feature approaches as well as convolutional neural
networks. In addition to the method and analysis, we propose a challenge like
nature to the new aggregated data set with almost half a million samples and
structured meta-data to encourage further research and societal engagement.

本文提出一种高效和准确的多任务学习方法，应用于艺术领域，并证明其在艺术数据上优于手工特征方法和卷积神经网络。此外，我们还提出了一个类似挑战性质的数据集，以激发更多的研究和社会参与。