HuggingFace Daily Papers·· 1 天前
综述：以世界建模统一视角看视频的读、写、共享与交互
Reading, Writing, Sharing, and Interacting with the World through Video
AI 导读
一篇 102 页综述以“世界建模”为统一视角梳理视频基础模型研究，把观察到的视频视为潜在时序世界的部分、有损、按时间排序的投影。文章围绕四种互补操作组织文献：reading 从视频推断世界属性、writing 按指定或学习到的世界条件合成视频、sharing 通过共通建模机制整合理解与生成、interacting 在动作与演化观察下预测并更新世界状态。
正文 · 原文
Abstract: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.
| Comments: | 102 pages, 17 figures, 19 tables. Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.31668 [cs.CV] |
| (or arXiv:2610.31668v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31668 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bobo Li [view email]
[v1]
Thu, 22 Oct 2026 17:59:50 UTC (19,671 KB)
来源：HuggingFace Daily Papers · arxiv.org