TY - JOUR

T1 - AI-Generated Content (AIGC) for Various Data Modalities

T2 - A Survey

AU - Foo, Lin Geng

AU - Rahmani, Hossein

AU - Liu, Jun

PY - 2025/5/6

Y1 - 2025/5/6

N2 - 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 potential of recent works, AIGC developments – especially in Machine Learning (ML) and Deep Learning (DL) – have been attracting significant attention, and this survey focuses on comprehensively reviewing such advancements in ML/DL. AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape, 3D scene, 3D human avatar, 3D motion, and audio – each presenting unique characteristics and challenges. Furthermore, there have been significant developments in cross-modality AIGC methods, where generative methods receive conditioning input in one modality and produce outputs in another. Examples include going from various modalities to image, video, 3D, and audio. This paper provides 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 discuss the typical applications of AIGC methods in various domains, challenges, and future research directions.

AB - 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 potential of recent works, AIGC developments – especially in Machine Learning (ML) and Deep Learning (DL) – have been attracting significant attention, and this survey focuses on comprehensively reviewing such advancements in ML/DL. AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape, 3D scene, 3D human avatar, 3D motion, and audio – each presenting unique characteristics and challenges. Furthermore, there have been significant developments in cross-modality AIGC methods, where generative methods receive conditioning input in one modality and produce outputs in another. Examples include going from various modalities to image, video, 3D, and audio. This paper provides 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 discuss the typical applications of AIGC methods in various domains, challenges, and future research directions.

U2 - 10.1145/3728633

DO - 10.1145/3728633

M3 - Journal article

VL - 57

JO - ACM Computing Surveys

JF - ACM Computing Surveys

SN - 0360-0300

IS - 9

M1 - 243

ER -