WorldPrompter: Traversable Text-to-Scene Generation

• URL: http://arxiv.org/abs/2504.02045v1
• Date: Wed, 02 Apr 2025 18:04:32 GMT
• Title: WorldPrompter: Traversable Text-to-Scene Generation
• Authors: Zhaoyang Zhang, Yannick Hold-Geoffroy, Miloš Hašan, Chen Ziwen, Fujun Luan, Julie Dorsey, Yiwei Hu,
• Abstract summary: We introduce WorldPrompter, a novel generative pipeline for synthesizing traversable 3D scenes from text prompts.<n>WorldPrompter incorporates a conditional 360deg panoramic video generator, capable of producing a 128-frame video that simulates a person walking through and capturing a virtual environment.<n>The resulting video is then reconstructed as Gaussian splats by a fast feedforward 3D reconstructor, enabling a true walkable experience within the 3D scene.
• Score: 18.405299478122693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene-level 3D generation is a challenging research topic, with most existing methods generating only partial scenes and offering limited navigational freedom. We introduce WorldPrompter, a novel generative pipeline for synthesizing traversable 3D scenes from text prompts. We leverage panoramic videos as an intermediate representation to model the 360{\deg} details of a scene. WorldPrompter incorporates a conditional 360{\deg} panoramic video generator, capable of producing a 128-frame video that simulates a person walking through and capturing a virtual environment. The resulting video is then reconstructed as Gaussian splats by a fast feedforward 3D reconstructor, enabling a true walkable experience within the 3D scene. Experiments demonstrate that our panoramic video generation model achieves convincing view consistency across frames, enabling high-quality panoramic Gaussian splat reconstruction and facilitating traversal over an area of the scene. Qualitative and quantitative results also show it outperforms the state-of-the-art 360{\deg} video generators and 3D scene generation models.

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