LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models
- URL: http://arxiv.org/abs/2603.07145v1
- Date: Sat, 07 Mar 2026 10:31:39 GMT
- Title: LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models
- Abstract summary: Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control.<n>They implicitly assume that the world only evolves within the observer's field of view.<n>Once an object leaves the observer's view, its state is "frozen" in memory, and revisiting the same region later often fails to reflect events that should have occurred in the meantime.<n>We propose LiveWorld, a novel framework that extends video world models to support persistent world evolution.
- Score: 32.92934803081681
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 field of view. Once an object leaves the observer's view, its state is "frozen" in memory, and revisiting the same region later often fails to reflect events that should have occurred in the meantime. In this work, we identify and formalize this overlooked limitation as the "out-of-sight dynamics" problem, which impedes video world models from representing a continuously evolving world. To address this issue, we propose LiveWorld, a novel framework that extends video world models to support persistent world evolution. Instead of treating the world as static observational memory, LiveWorld models a persistent global state composed of a static 3D background and dynamic entities that continue evolving even when unobserved. To maintain these unseen dynamics, LiveWorld introduces a monitor-based mechanism that autonomously simulates the temporal progression of active entities and synchronizes their evolved states upon revisiting, ensuring spatially coherent rendering. For evaluation, we further introduce LiveBench, a dedicated benchmark for the task of maintaining out-of-sight dynamics. Extensive experiments show that LiveWorld enables persistent event evolution and long-term scene consistency, bridging the gap between existing 2D observation-based memory and true 4D dynamic world simulation. The baseline and benchmark will be publicly available at https://zichengduan.github.io/LiveWorld/index.html.
Related papers
- Programmable World Model [37.43654073749753]
We introduce Programmable World Model, a framework that decouples world-state evolution from visual observation generation.<n>An agent translates natural-language instructions into executable programs that specify entity states and state-transition rules.<n>A lightweight engine executes these programs to update and maintain an explicit, persistent global world state.
arXiv Detail & Related papers (2026-09-09T17:59:32Z) - Code World Model: Coding Agent as World Brain [51.31807545747191]
We introduce Code World Model, a framework that separates world evolution from visual realization.<n>A coding agent serves as the world brain, reasoning about events and their consequences.<n>To connect executable state with visual generation, we introduce a proxy representation that encodes frame-wisetemporal constraints into a proxy video.
arXiv Detail & Related papers (2026-08-26T15:37:33Z) - WorldDirector: Building Controllable World Simulators with Persistent Dynamic Memory [106.28234880163723]
WorldDirector is a highly controllable video world model framework.<n>Our framework explicitly decouples semantic motion orchestration from visual generation.<n>We successfully preserve the exact visual identities of dynamic entities even when they re-enter the scene after prolonged periods out of view.
arXiv Detail & Related papers (2026-07-02T17:59:59Z) - Current World Models Lack a Persistent State Core [18.34296893231955]
World models are increasingly regarded as a decisive step toward artificial general intelligence.<n>They require an internal world state that keeps evolving over time, decoupled from observation.<n>We introduce textbfWRBench, the first systematic diagnostic benchmark that treats camera motion as an intervention on observability.
arXiv Detail & Related papers (2026-06-18T17:55:15Z) - MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data [125.43597497646444]
MetaWorld is a novel framework that scales multi-agent video world models to open-domain environments directly from single-view videos.<n>It achieves superior cross-view consistency and identity fidelity, establishing a highly scalable, physics-driven paradigm for multi-agent video world modeling.
arXiv Detail & Related papers (2026-06-01T18:20:20Z) - World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning [18.658825757342544]
Vision-language models (VLMs) have shown strong performance on static visual understanding.<n>They still struggle with dynamic spatial reasoning that requires imagining how scenes evolve under egocentric motion.<n>We propose World2VLM, a training framework that distills spatial imagination from a generative world model into a vision-language model.
arXiv Detail & Related papers (2026-04-29T17:48:01Z) - Beyond Pixel Histories: World Models with Persistent 3D State [50.4601060508243]
PERSIST is a new paradigm of world model which simulates the evolution of a latent 3D scene.<n>We show substantial improvements in spatial memory, 3D consistency, and long-horizon stability over existing methods.
arXiv Detail & Related papers (2026-03-03T19:58:31Z) - TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model [53.555353366322464]
We present TeleWorld, a real-time multimodal 4D world modeling framework that unifies video generation, dynamic scene reconstruction, and long-term world memory within a closed-loop system.<n>Our approach achieves seamless integration of dynamic object modeling and static scene representation within a unified 4D framework, advancing world models toward practical, interactive, and computationally accessible synthesis systems.
arXiv Detail & Related papers (2025-12-31T18:31:46Z) - PAN: A World Model for General, Interactable, and Long-Horizon World Simulation [49.805071498152536]
We introduce PAN, a general, interactable, and long-horizon world model.<n>It predicts future world states through high-quality video simulation conditioned on history and natural language actions.<n>Experiments show that PAN achieves strong performance in action-conditioned world simulation, long-horizon forecasting, and simulative reasoning.
arXiv Detail & Related papers (2025-11-12T07:20:35Z) - Simulating the Visual World with Artificial Intelligence: A Roadmap [48.64639618440864]
Video generation is shifting from generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility.<n>This survey provides a systematic overview of this evolution, conceptualizing modern video foundation models as the combination of two core components.<n>We trace the progression of video generation through four generations, culminating in a video generation model that embodies intrinsic physical plausibility.
arXiv Detail & Related papers (2025-11-11T18:59:50Z) - Learning 3D Persistent Embodied World Models [84.40585374179037]
We introduce a new persistent embodied world model with an explicit memory of previously generated content.<n>During generation time, our video diffusion model predicts RGB-D video of the future observations of the agent.<n>This generation is then aggregated into a persistent 3D map of the environment.
arXiv Detail & Related papers (2025-05-05T17:59:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.