Abstract
Video world models are moving toward preserving an observed world under controllable camera and object motion while allowing its environmental state to change. Yet these controls remain isolated, and weather generation typically relies on a source video or reconstructed scene that already specifies future structure. We study a first-frame-anchored source-to-state setting, where the model starts from a single image and follows explicit camera and object controls and an optional weather instruction, then generates a video that either preserves the source world or transfers it to a target weather state. To address these challenges, we first build HoloStateData, a state video dataset that turns diverse videos into unified control samples for camera, object, and weather supervision. Second, we introduce Holo-World, a unified controllable video world model that jointly controls scene from a single image. Its Unified Scene Adapter factorizes world preservation and weather transfer into distinct parameter subspaces, using rendered background, geometry buffers, and object controls to maintain controlled scene structure while modeling weather-dependent appearance and particle effects. Additionally, Scene-Weather Decomposed CFG guides scene and weather residuals separately, strengthening target weather effects without over-amplifying the full condition. Quantitative and qualitative experiments demonstrate that Holo-World maintains precise camera and object control with consistent scene structure while transferring scenes into diverse target weather state, outperforming video-to-video weather editing baselines on weather-state generation. Our …
本文提出Holo-World，一种统一的视频世界模型，可从单张图像出发，联合控制相机运动、物体动态和天气状态。作者构建了HoloStateData数据集，将多样视频转换为统一控制样本；并提出统一场景适配器，将世界保持与天气迁移分解到不同参数子空间，利用渲染背景、几何缓冲和物体控制维持场景结构，同时建模天气相关外观与粒子效果。场景-天气分解CFG进一步分别引导场景和天气残差，增强目标天气效果。实验表明，Holo-World在保持精确控制的同时，实现了优于视频到视频基线的天气状态生成。