Image and shape editing are ubiquitous among digital artworks. Graphics algorithms facilitate artists and designers to achieve desired editing intents without going through manually tedious retouching. In the recent advance of machine learning, artists' editing intents can even be driven by text, using a variety of well-trained neural networks. They have seen to be receiving an extensive success on such as generating photorealistic images, artworks and human poses, stylizing meshes from text, or auto-completion given image and shape priors. In this short survey, we provide an overview over 50 papers on state-of-the-art (text-guided) image-and-shape generation techniques. We start with an overview on recent editing algorithms in the introduction. Then, we provide a comprehensive review on text-guided editing techniques for 2D and 3D independently, where each of its sub-section begins with a brief background introduction. We also contextualize editing algorithms under recent implicit neural representations. Finally, we conclude the survey with the discussion over existing methods and potential research ideas.

翻译：图像与形状编辑在数字艺术作品中无处不在。图形学算法帮助艺术家和设计师实现所需的编辑意图，而无需进行繁琐的手动修饰。在近期机器学习的进展中，借助多种训练有素的神经网络，艺术家的编辑意图甚至可以由文本驱动。这些技术已在诸多领域取得显著成功，例如生成逼真图像、艺术作品和人体姿态，从文本风格化网格，或基于图像和形状先验进行自动补全。在这篇短篇综述中，我们概述了50余篇关于最先进（文本引导）图像与形状生成技术的论文。我们首先在引言中概述了近期编辑算法，随后分别对2D和3D的文本引导编辑技术进行了全面回顾，每个子章节均以简要背景介绍开篇。我们还讨论了近期隐式神经表示框架下的编辑算法。最后，我们通过对现有方法和潜在研究思路的讨论为综述作结。