VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

• URL: http://arxiv.org/abs/2601.05138v1
• Date: Thu, 08 Jan 2026 17:28:52 GMT
• Title: VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control
• Authors: Sixiao Zheng, Minghao Yin, Wenbo Hu, Xiaoyu Li, Ying Shan, Yanwei Fu,
• Abstract summary: VerseCrafter is a 4D-aware video world model that enables explicit and coherent control over both camera and object dynamics.<n>Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state through a static background point cloud.<n>These 4D controls are rendered into conditioning signals for a pretrained video diffusion model, enabling the generation of high-fidelity, view-consistent videos.
• Score: 83.92729346325163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently operate dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a 4D-aware video world model that enables explicit and coherent control over both camera and object dynamics within a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state through a static background point cloud and per-object 3D Gaussian trajectories. This representation captures not only an object's path but also its probabilistic 3D occupancy over time, offering a flexible, category-agnostic alternative to rigid bounding boxes or parametric models. These 4D controls are rendered into conditioning signals for a pretrained video diffusion model, enabling the generation of high-fidelity, view-consistent videos that precisely adhere to the specified dynamics. Unfortunately, another major challenge lies in the scarcity of large-scale training data with explicit 4D annotations. We address this by developing an automatic data engine that extracts the required 4D controls from in-the-wild videos, allowing us to train our model on a massive and diverse dataset.

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