  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.
