  AI-generated content (AIGC) methods aim to produce text, images, videos, 3D
assets, and other media using AI algorithms. Due to its wide range of
applications and the demonstrated potential of recent works, AIGC developments
have been attracting lots of attention recently, and AIGC methods have been
developed for various data modalities, such as image, video, text, 3D shape (as
voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human
avatar (body and head), 3D motion, and audio -- each presenting different
characteristics and challenges. Furthermore, there have also been many
significant developments in cross-modality AIGC methods, where generative
methods can receive conditioning input in one modality and produce outputs in
another. Examples include going from various modalities to image, video, 3D
shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar),
and audio modalities. In this paper, we provide a comprehensive review of AIGC
methods across different data modalities, including both single-modality and
cross-modality methods, highlighting the various challenges, representative
works, and recent technical directions in each setting. We also survey the
representative datasets throughout the modalities, and present comparative
results for various modalities. Moreover, we also discuss the challenges and
potential future research directions.
