EvoWorld: Towards Out-of-Sight World Evolution in Generative Video Models
EvoWorld: Towards Out-of-Sight World Evolution in Generative Video Models
EvoWorld combines a visual language model for maintaining and reasoning about world states with a segment-based autoregressive video renderer to improve causal evolution and the coherence of revisiting generated video world simulators under event conditions. Existing diffusion-based video models lack explicit mechanisms for maintaining world states and reasoning about the consequences of event conditions, often producing visually plausible but behaviorally static or causally inconsistent outputs. EvoWorld significantly improves causal evolution under event conditions by observing generated segments, maintaining records of world evolution, and reasoning about possible state transitions, while maintaining visual quality and coherence.
Published on Nov 4
Authors:
,
,
,
,
,
Abstract
EvoWorld combines a vision-language model that maintains and reasons over world state with a segment-autoregressive video renderer to improve event-conditioned causal evolution and revisit consistency in generative video world simulators.
Generative video models are increasingly used as visual world simulators, yet their primary strength lies in rendering plausible pixels rather than reasoning about how a world should evolve. Coherent world evolution requires more than visual fidelity: when an event occurs, a model must infer its consequences for the entities in the world and then render the resulting state. For example, after a wild beast appears, the model should not only depict the wild beast, but also infer whether a nearby person should flee, take cover, or fight back based on the realized scene. Existing diffusion-based video models lack an explicit mechanism for maintaining world state and reasoning over such event-conditioned consequences, often producing visually plausible but behaviorally static or causally inconsistent outputs. To make this reasoning gap concrete and measurable, we propose a new problem, Out-of-Sight Causal Evolution, which requires a model to render an event, reason about its consequences for the evolving world state, and reveal a causally plausible outcome as the world continues to evolve, even out of sight. As a baseline reference solution, we introduce EvoWorld, a brain-renderer collaboration framework in which a vision-language model serves as the world brain: it observes generated segments, maintains an evolving account of the world, and reasons about plausible state transitions, while a segment-autoregressive video renderer realizes them visually. We further introduce EvoWorld-Data and EvoWorld-Bench for training and evaluating event realization, causal reactions, and revisit consistency. Experiments show that EvoWorld substantially improves event-conditioned causal evolution over recent models while preserving visual quality and consistency, establishing a reference point for reasoning-guided, autonomous world evolution beyond the current field of view.
View arXiv page View PDF Project page GitHub 22 Add to collection
Get this paper in your agent:
hf papers read 2611.06748
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 1
Image-to-Video • Updated 4 days ago • 1Tommy-Bie/EvoWorld
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2611.06748 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2611.06748 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
Source: Hugging Face Daily Papers · huggingface.co