arXiv Computer Vision

Can Video World Models Track Unobserved World States?

arXiv Computer Vision
Sep 11

World in World: Explore the World with World Models

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
arXiv Computer Vision
Sep 4

Do Video Generators Track the World Across Segments? A Benchmark and Method for World-State Reasoning in Video Continuation

The paper introduces Statebench, a benchmark for evaluating how well video generators track world states across segments, focusing on past-visible, occluded-process, and complex-transition states. It also proposes Stateagent, a method that maintains an explicit entity-state representation, updates it with new prompts, and uses the resulting state to guide video continuation. Experiments show Stateagent raises the overall state score from 45.2 to 69.3 and improves one‑minute story generation.

By Yingmao Miao, Pengfei Zhang, Chaoran Xu, Meng Yu, Jing Tang, Xiangxiang Chu, Chao Shen, Chenhao Lin
arXiv AI
Jul 22

AlayaWorld: Interactive Long-Horizon World Modeling -- Full Technical Report

arXiv:2607. 18367v1 Announce Type: new Abstract: Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models generate interactive environments from user inputs instantly.

By AlayaWorld Team, Kaipeng Zhang, Chuanhao Li, Yifan Zhan, Yongtao Ge, Yuanyang Yin, Jiaming Tan, Kang He, Liaoyuan Fan, Mingliang Zhai, Ruicong Liu, Xiaojie Xu, Xuangeng Chu, Zhen Li, Zhengyuan Lin, Zhixiang Wang, Zian Meng, Zihui Gao
Hugging Face Trending Papers
Sep 10

World in World: Explore the World with World Models

World in World introduces a training‑free inference interface that lets users control autoregressive video world models from new viewpoints. By converting diverse control signals—source‑video observations, target‑view projections, geometry renderings, and retrieved states—into camera‑ and time‑labelled visual tokens, the system uses a frozen causal video model’s self‑attention to maintain synchronization, complete unseen regions, and recover past appearances. The method supports camera‑controlled rerendering, long‑horizon revisiting, and human‑motion transfer while preserving perceptual quality, temporal consistency, and camera‑following accuracy.

arXiv Machine Learning
Jun 9

Echo-Memory: A Controlled Study of Memory in Action World Models

arXiv:2606. 09803v1 Announce Type: cross Abstract: We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models.

By Wayne King, Zeyue Xue, Yuxuan Bian, Jie Huang, Haoran Li, Yaowei Li, Yaofeng Su, Yuming Li, Haoyu Wang, Shiyi Zhang, Songchun Zhang, Yuwei Niu, Sihan Xu, Junhao Zhuang, Haoyang Huang, Nan Duan