What is it about?
Video generation has emerged as a dominant direction within the field of multimodal generation today. A central challenge in current research lies in how to maintain spatiotemporal consistency during the content generation process. This paper presents a systematic review of relevant methodologies, aiming to assist readers in efficiently navigating and grasping the key technologies and developmental trajectory of this field.
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Why is it important?
This paper is the first to conceptualize the task of video generation as a process of sampling from high-dimensional spatiotemporal distributions; furthermore, it systematically reviews the latest advancements in maintaining spatiotemporal consistency across multiple dimensions—including generation models, feature representations, training strategies and so on—thereby filling a gap in the literature regarding systematic surveys within this core domain.
Perspectives
Spatiotemporal consistency constitutes a core challenge in video generation, and numerous excellent works have already been dedicated to addressing this problem. Our team aims to systematically review these research findings, providing clear guidance for newcomers to the field and fostering its further advancement.
Zhiyu Yin
Harbin Institute of Technology
Read the Original
This page is a summary of: A Survey: Spatiotemporal Consistency in Video Generation, ACM Computing Surveys, April 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3802588.
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