NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation

2026-08-25

AI Summary

为了解决单目图像转换为可模拟场景的难题，NeoWorld-Pro利用MLLMs将图像转化为包含物体几何、关节和物理属性的程序，并通过物理引擎迭代优化。

Abstract

The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual observations into simulation-ready scenes remains challenging due to the lack of physical grounding and scene-level interactivity in current image-to-URDF methods. We propose NeoWorld-Pro, a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments. Leveraging the zero-shot reasoning and code synthesis capabilities of MLLMs, NeoWorld-Pro converts a single RGB image into executable programs specifying object geometry, articulation, and physical properties. A physics-in-the-loop mechanism then iteratively refines the generated programs by validating their execution in a physics engine, enforcing physically plausible articulations, valid object compositions and interactions, and accurate spatial relationships. Experiments show that NeoWorld-Pro outperforms open-loop and prior monocular reconstruction methods, while enabling complex downstream tasks such as stable stacking and fine-grained manipulation.

Problem

• Embodied AI
• monocular images
• scene reconstruction
• physical grounding
• scene-level interactivity

Innovation

• procedural programming
• zero-shot reasoning
• code synthesis
• physics-in-the-loop