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The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence…

Artificial Intelligence · Computer Science 2026-02-09 Jingtong Yue , Ziqi Huang , Zhaoxi Chen , Xintao Wang , Pengfei Wan , Ziwei Liu

The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as potential world simulators. However, a critical gap still remains between the theoretical…

Image and Video Processing · Electrical Eng. & Systems 2026-05-06 Muyang He , Hanzhong Guo , Junxiong Lin , Yizhou Yu

Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical cognition have gradually received widespread attention -…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Minghui Lin , Xiang Wang , Yishan Wang , Shu Wang , Fengqi Dai , Pengxiang Ding , Cunxiang Wang , Zhengrong Zuo , Nong Sang , Siteng Huang , Donglin Wang

Video generation models have emerged as high-fidelity models of the physical world, capable of synthesizing high-quality videos capturing fine-grained interactions between agents and their environments conditioned on multi-modal user…

A world model is an AI system that simulates how an environment evolves under actions, enabling planning through imagined futures rather than reactive perception. Current world models, however, suffer from visual conflation: the mistaken…

Artificial Intelligence · Computer Science 2026-01-23 Zhikang Chen , Tingting Zhu

We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time…

Computer Vision and Pattern Recognition · Computer Science 2022-06-10 Tim Brooks , Janne Hellsten , Miika Aittala , Ting-Chun Wang , Timo Aila , Jaakko Lehtinen , Ming-Yu Liu , Alexei A. Efros , Tero Karras

Advancements in language foundation models have primarily fueled the recent surge in artificial intelligence. In contrast, generative learning of non-textual modalities, especially videos, significantly trails behind language modeling. This…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Lijun Yu

A world model matters to an agent only through the state it constructs. That state must preserve some information, discard other information, and support some future function: prediction, control, planning, memory, grounding, or…

Artificial Intelligence · Computer Science 2026-05-05 Keon Woo Kim

Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning, persistent state, long-horizon consistency, and causal…

Emerging world models autoregressively generate video frames in response to actions, such as camera movements and text prompts, among other control signals. Due to limited temporal context window sizes, these models often struggle to…

Computer Vision and Pattern Recognition · Computer Science 2025-06-06 Tong Wu , Shuai Yang , Ryan Po , Yinghao Xu , Ziwei Liu , Dahua Lin , Gordon Wetzstein

Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities. However, existing models typically lack a 3D representation of the environment, meaning 3D consistency must…

Computer Vision and Pattern Recognition · Computer Science 2026-03-05 Samuel Garcin , Thomas Walker , Steven McDonagh , Tim Pearce , Hakan Bilen , Tianyu He , Kaixin Wang , Jiang Bian

Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work,…

World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning. However, current world model research is often dominated by three partially…

Artificial Intelligence · Computer Science 2026-05-27 Sen Cui , Jingheng Ma

Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control. However, they implicitly assume that the world only evolves within the observer's…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zicheng Duan , Jiatong Xia , Zeyu Zhang , Wenbo Zhang , Gengze Zhou , Chenhui Gou , Yefei He , Feng Chen , Xinyu Zhang , Lingqiao Liu

Predicting the dynamics of interacting objects is essential for both humans and intelligent systems. However, existing approaches are limited to simplified, toy settings and lack generalizability to complex, real-world environments. Recent…

Computer Vision and Pattern Recognition · Computer Science 2025-04-07 Rick Akkerman , Haiwen Feng , Michael J. Black , Dimitrios Tzionas , Victoria Fernández Abrevaya

We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative…

Computer Vision and Pattern Recognition · Computer Science 2016-10-27 Carl Vondrick , Hamed Pirsiavash , Antonio Torralba

A world model enables an intelligent agent to imagine, predict, and reason about how the world evolves in response to its actions, and accordingly to plan and strategize. While recent video generation models produce realistic visual…

Video world models have achieved strong visual realism, but this does not ensure that their dynamics are truly governed by actions. In this work, we argue that action faithfulness should be understood through the compositional structure of…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Zijie Wang , Wei Zhang , Weiming Zhang , Fanqi Zhang , Xiao Tan , Yipeng Qin , Guanbin Li

Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Ying Shen , Jerry Xiong , Tianjiao Yu , Ismini Lourentzou

We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Yihong Sun , Hao Zhou , Liangzhe Yuan , Jennifer J. Sun , Yandong Li , Xuhui Jia , Hartwig Adam , Bharath Hariharan , Long Zhao , Ting Liu
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