arXiv:2606.24799v1 Announce Type: cross Abstract: Generic text-to-video models can be used as rich open-world scene priors. Despite the high quality of today's generated videos, they do not directly yield reliable 3D assets: camera motion is difficult to control, view coverage is partial, and frames often contain inconsistencies across time. We introduce OrbitForge, an adapter built from frozen video priors and per-prompt Gaussian Splatting reconstruction optimization that converts a single text-generated video into a canonical closed-orbit 3D Gaussian Splatting scene. We use 3D reconstruction
The rapid advancement in text-to-video models and 3D reconstruction techniques, particularly Gaussian Splatting, has created the necessary algorithmic foundation for integrating these capabilities into a coherent text-to-3D scene generation pipeline.
This development pushes the frontier of AI-generated content beyond flat videos into controllable, explorable 3D environments, which is critical for foundational applications in spatial computing, gaming, e-commerce, and industrial design.
The ability to generate 3D scenes directly from text with controllable camera motion and view consistency transforms previous text-to-video limitations into a more robust 3D asset creation process.
- · Generative AI platforms
- · 3D content creators
- · VR/AR developers
- · Gaming industry
- · Traditional 3D modeling pipelines
- · Manual asset creation studios
Creation of complex, dynamic 3D environments becomes significantly faster and more accessible to non-experts.
This democratizes spatial content creation, leading to a proliferation of virtual worlds and digital twins that are indistinguishable from reality.
The integration of these generated 3D assets into AI agents could enable true autonomous 'digital beings' operating within rich, contextualized virtual environments, eventually bridging to the physical world.
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Read at arXiv cs.AI