Monday, 26 October 2026 · 6 min read
Survey unifies video understanding and generation via world modeling framework
This survey organizes video foundation models around four operations—reading, writing, sharing, and interacting—viewing video as a partial observation of a latent world, and highlights shifts toward persistent, controllable, and physically consistent video modeling.
Based on the paper Reading, Writing, Sharing, and Interacting with the World through Video by Shengqiong Wu, Meng Luo, Bobo Li, Ziwei Liu and 4 others
Video modeling challenges and the latent world-view
Video captures dynamic scenes with evolving entities and actions over time, making it richer than static images or text but also more complex to model due to partial observability and lossy sensing.
Video foundation models have advanced in understanding (e.g., action recognition, question answering) and generation (e.g., video synthesis), but these tasks traditionally focus on clip-level recognition or visual fidelity rather than modeling the underlying evolving world state.
A latent temporal world underlies observed video as a partial, lossy, temporally ordered projection; modeling this latent world requires inferring persistent states, predicting their evolution, and ensuring physical and causal consistency, which is challenging due to occlusions, viewpoint changes, and incomplete observations.
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- How the method works, step by step, with a flow chart
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