arXiv:2610.19063v1 Announce Type: new
Abstract: As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requi...
By Nan Zhou, Jianxiong Gao, Qiuyu Wang, Wen Wang, Jiahao Wang, Yufeng Yuan, Shuaiting Li, Hao Ouyang, Yujun Shen, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
arXiv:2605.15618v2 Announce Type: replace-cross
Abstract: Self-supervised video models are increasingly framed as world models, yet they are still evaluated almost entirely on clean video and reporte...
By Ali J Alrasheed, Aryan Yazdan Parast, Basim Azam, James Bailey, Naveed Akhtar
arXiv:2606. 10620v1 Announce Type: cross Abstract: Image generation models now produce high-quality static images, yet their ability to represent how a visual world changes over time remains poorly understood.
By Xinrui Wu, Lichen Huang
World in World introduces a training‑free, inference‑time interface that transforms diverse control signals—such as source‑video observations, target‑view projections, geometry renderings, and retrieved states—into camera‑ and time‑labelled visual states. These states are processed by a frozen causal video model’s self‑attention, enabling tasks like camera‑controlled rerendering, long‑horizon revisiting, and human‑motion transfer without additional training. The method employs a correspondence router and evidence‑wise attention to align token identities and regulate auxiliary channel contributions during a single denoising pass.
By Chenxi Song, Yanming Yang, Chi Zhang
StateCraft introduces a new architecture that separates a persistent, updatable abstract state from the visual state in video world models. By pairing a language model with a video diffusion model, it tracks entity changes over time and conditions visual generation on this abstract state. The approach is evaluated on generative interfaces and a new benchmark, showing improved generation quality, memory usage, and controllability, while enabling direct editing of unseen states and extending to spatial 3D environments.
By Lorenzo Venturoli, Nedko Savov, Xi Wang, Marc Pollefeys, Luc Van Gool
arXiv:2511.17481v2 Announce Type: replace
Abstract: World models learn to predict the temporal evolution of visual observations given a control signal, potentially enabling agents to reason about env...
By Yiqing Shen, Aiza Maksutova, Chenjia Li, Mathias Unberath