Text2Immersion: Generative Immersive Scene with 3D Gaussians

Hao Ouyang1,2, Stephen Lombardi1, Kathryn Heal1, <<<<<<< HEAD ======= >>>>>>> f07268c3589edfcdde755f66487eba619427bc9d Tiancheng Sun1,
1Google 2HKUST

Outdoor scene generation

Text prompt: "Autumn park", "Cabin in the wood", "Cabin in the wood".


Indoor scene generation

Text prompt: "A cozy living room", "A cozy living room", "Japanese style room".


Stylized scene generation

Text prompt: "Watercolor night street", "Future night city", "Heaven".


Abstract



We introduce Text2Immersion, an elegant method for producing high-quality 3D immersive scenes from text prompts. Our proposed pipeline initiates by progressively generating a Gaussian cloud using pre-trained 2D diffusion and depth estimation models. This is followed by a refining stage on the Gaussian cloud, interpolating and refining it to enhance the details of the generated scene. Distinct from prevalent methods that focus on single object or indoor scenes, or employ zoom-out trajectories, our approach generates diverse scenes with various objects, even extending to the creation of imaginary scenes. Consequently, Text2Immersion can have wide-ranging implications for various applications such as virtual reality, game development, and automated content creation. Extensive evaluations demonstrate that our system surpasses other methods in rendering quality and diversity, further progressing towards text-driven 3D scene generation.



BibTeX

@article{ouyang2023text,
  author    = {Ouyang, Hao and Sun, Tiancheng and Lombardi, Stephen and Heal, Kathryn},
  title     = {Text2Immersion: Generative Immersive Scene with 3D Gaussians},
  journal   = {Arxiv},
  year      = {2023},
}