The rapid developments of artificial intelligence have significantly impacted daily life and content production modes.In the field of video generation,researchers are now exploring this emerging technique with innovat...The rapid developments of artificial intelligence have significantly impacted daily life and content production modes.In the field of video generation,researchers are now exploring this emerging technique with innovative approaches,aiming to produce videos of higher quality,longer duration,and greater diversity.Currently,numerous video generation algorithms have been developed using different architecture designs.Unlike image generation,video generation requires maintaining consistency across both spatial and temporal dimensions while ensuring aesthetic quality and dynamic coherence,making it a more challenging task.In this survey,we provide a systematic review of existing video generation methods,tracing their evolution across different architectural paradigms.We further categorize recent models by their control conditions(e.g.,text-to-video,image to-video,multi-modal guidance)and summarize their unique theoretical foundations,architectural designs,and algorithmic innovations.In the meantime,we review the commonly used video datasets and analyze their applicability to different tasks.We also present evaluations of representative models to offer a more comprehensive perspective.Our goal is to provide a clear and concise overview of these algorithms,offering insights to support future breakthroughs in video generation.展开更多
基金Supported by“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant No.2023C01181)National Natural Science Foundation of China(Grant Nos.62421003,62302449)Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ23F020009).
文摘The rapid developments of artificial intelligence have significantly impacted daily life and content production modes.In the field of video generation,researchers are now exploring this emerging technique with innovative approaches,aiming to produce videos of higher quality,longer duration,and greater diversity.Currently,numerous video generation algorithms have been developed using different architecture designs.Unlike image generation,video generation requires maintaining consistency across both spatial and temporal dimensions while ensuring aesthetic quality and dynamic coherence,making it a more challenging task.In this survey,we provide a systematic review of existing video generation methods,tracing their evolution across different architectural paradigms.We further categorize recent models by their control conditions(e.g.,text-to-video,image to-video,multi-modal guidance)and summarize their unique theoretical foundations,architectural designs,and algorithmic innovations.In the meantime,we review the commonly used video datasets and analyze their applicability to different tasks.We also present evaluations of representative models to offer a more comprehensive perspective.Our goal is to provide a clear and concise overview of these algorithms,offering insights to support future breakthroughs in video generation.
