Abstract
Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared world modeling, where a model generates multiple videos from a set of input images, each representing the same underlying world in different camera poses. We propose IC-World, a novel generation framework, enabling parallel generation for all input images via activating the inherent in-context generation capability of large video models. We further finetune IC-World via reinforcement learning, Group Relative Policy Optimization, together with two proposed novel reward models to enforce scene-level geometry consistency and object-level motion consistency among the set of generated videos. Extensive experiments demonstrate that IC-World substantially outperforms state-of-the-art methods in both geometry and motion consistency. To the best of our knowledge, this is the first work to systematically explore the shared world modeling problem with video-based world models.
Abstract (translated)
基于视频的世界模型因其能够合成多样且动态的视觉环境而最近越来越受到关注。在本文中，我们专注于共享世界建模，其中模型从一组输入图像生成多个视频，每个视频代表同一基本世界的不同相机视角下的场景。为此，我们提出了一种新的生成框架——IC-World，通过激活大型视频模型中的内在上下文生成能力来实现所有输入图像的并行生成。此外，我们还使用强化学习方法（Group Relative Policy Optimization）对IC-World进行微调，并结合两个新提出的奖励模型以确保生成的一组视频在场景级别几何一致性和对象级别运动一致性方面达到更好的效果。大量的实验表明，IC-World在几何和运动一致性方面的表现显著优于现有最先进的方法。据我们所知，这是首次系统性地探索基于视频的世界模型中的共享世界建模问题的研究工作。
URL
https://arxiv.org/abs/2512.02793
