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GenEx: Generating an Explorable World

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arxiv 2412.09624 v4 pith:24B56DHX submitted 2024-12-12 cs.CV cs.RO

classification cs.CVcs.RO
keywords worldgenexagentsembodiedexplorationgenerativephysicalcapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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Understanding, navigating, and exploring the 3D physical real world has long been a central challenge in the development of artificial intelligence. In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms priors (expectations) about the surrounding environments. GenEx generates an entire 3D-consistent imaginative environment from as little as a single RGB image, bringing it to life through panoramic video streams. Leveraging scalable 3D world data curated from Unreal Engine, our generative model is rounded in the physical world. It captures a continuous 360-degree environment with little effort, offering a boundless landscape for AI agents to explore and interact with. GenEx achieves high-quality world generation, robust loop consistency over long trajectories, and demonstrates strong 3D capabilities such as consistency and active 3D mapping. Powered by generative imagination of the world, GPT-assisted agents are equipped to perform complex embodied tasks, including both goal-agnostic exploration and goal-driven navigation. These agents utilize predictive expectation regarding unseen parts of the physical world to refine their beliefs, simulate different outcomes based on potential decisions, and make more informed choices. In summary, we demonstrate that GenEx provides a transformative platform for advancing embodied AI in imaginative spaces and brings potential for extending these capabilities to real-world exploration.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A pipeline that restores corrupted novel-view videos with a video diffusion model and jointly denoises multiple viewpoints to improve 3D scene exploration from a single image.

  2. PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation

    cs.CV 2025-09 reject novelty 5.0 of 10

    Fine-tuning a pretrained video diffusion model with LoRA rank 16 on about 1,000 synthetic videos produces panoramic video with good seam closure, but the claim that rank must exceed 8 degrees of freedom is not proven.

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