StableWorld: Towards Stable and Consistent Long Interactive Video Generation
Ying Yang, Zhengyao Lv, Tianlin Pan, Haofan Wang, Binxin Yang, Hubery Yin, Chen Li, Ziwei Liu, Chenyang Si

TL;DR
StableWorld introduces a dynamic frame eviction method to enhance stability and temporal consistency in long interactive video generation, addressing error accumulation and scene drift issues.
Contribution
The paper proposes StableWorld, a simple yet effective frame filtering technique that improves stability and consistency across various interactive video models.
Findings
Significantly improves stability in multiple models
Reduces error accumulation and scene drift
Enhances generalization across scenarios
Abstract
In this paper, we explore the overlooked challenge of stability and temporal consistency in interactive video generation, which synthesizes dynamic and controllable video worlds through interactive behaviors such as camera movements and text prompts. Despite remarkable progress in world modeling, current methods still suffer from severe instability and temporal degradation, often leading to spatial drift and scene collapse during long-horizon interactions. To better understand this issue, we initially investigate the underlying causes of instability and identify that the major source of error accumulation originates from the same scene, where generated frames gradually deviate from the initial clean state and propagate errors to subsequent frames. Building upon this observation, we propose a simple yet effective method, \textbf{StableWorld}, a Dynamic Frame Eviction Mechanism. By…
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Taxonomy
TopicsHuman Motion and Animation · Artificial Intelligence in Games · Music Technology and Sound Studies
