通过视频阅读、写作、分享和交互理解世界
Reading, Writing, Sharing, and Interacting with the World through Video
这篇综述将视频理解和生成统一于世界建模，围绕推断、合成、分享和交互潜在时间状态来组织进展，强调持续动态而非片段级感知。视频基础模型在理解和生成视觉序列方面迅速发展，而视频世界模型试图捕捉其描绘世界的底层动态。
Abstract
This survey unifies video understanding and generation through world modeling, organizing progress around inferring, synthesizing, sharing, and interacting with latent temporal states to emphasize persistent dynamics over clip-level perception.
Video foundation models have advanced rapidly in understanding and generating visual sequences, while video world models seek to capture the underlying dynamics of the world they depict. Despite their different objectives, both concern the relationship between video observations and an evolving world. This survey adopts world modeling as a unifying lens for examining these developments. Viewing an observed video as a partial, lossy, and temporally ordered projection of a latent temporal world, we organize the literature around four complementary operations: reading infers world properties from observed video; writing synthesizes video from specified or learned world conditions; sharing integrates understanding and generation through common modeling mechanisms; and interacting predicts and updates world states under actions and evolving observations. This perspective distinguishes temporal coherence in observed or generated sequences from consistency in the underlying world state. Across tasks, representations, models, datasets, and benchmarks, we trace a shift in emphasis from clip-level recognition and perceptual fidelity toward persistent state, controllable evolution, physical and causal validity, and evaluation grounded in interaction and downstream decisions, and we distill the open challenges that follow.
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