WorldAgents: Can Foundation Image Models be Agents for 3D World Models?
This addresses the problem of leveraging 2D models for 3D world generation in computer vision and AI, offering a novel method to exploit implicit 3D understanding.
The paper investigated whether 2D foundation image models inherently possess 3D world model capabilities by evaluating them on 3D world synthesis, and demonstrated that an agentic approach using VLMs and image generators can produce coherent and robust 3D reconstructions with expansive, realistic, and 3D-consistent worlds.
Given the remarkable ability of 2D foundation image models to generate high-fidelity outputs, we investigate a fundamental question: do 2D foundation image models inherently possess 3D world model capabilities? To answer this, we systematically evaluate multiple state-of-the-art image generation models and Vision-Language Models (VLMs) on the task of 3D world synthesis. To harness and benchmark their potential implicit 3D capability, we propose an agentic framing to facilitate 3D world generation. Our approach employs a multi-agent architecture: a VLM-based director that formulates prompts to guide image synthesis, a generator that synthesizes new image views, and a VLM-backed two-step verifier that evaluates and selectively curates generated frames from both 2D image and 3D reconstruction space. Crucially, we demonstrate that our agentic approach provides coherent and robust 3D reconstruction, producing output scenes that can be explored by rendering novel views. Through extensive experiments across various foundation models, we demonstrate that 2D models do indeed encapsulate a grasp of 3D worlds. By exploiting this understanding, our method successfully synthesizes expansive, realistic, and 3D-consistent worlds.