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.
我们介绍了一种名为Text2Immersion的优雅方法，用于从文本提示生成高质量的3D沉浸式场景。我们提出的流程首先使用预训练的2D扩散和深度估计模型逐步生成高斯云。这之后是对高斯云的细化阶段，对其进行插值和细化，以增强生成场景的细节。与侧重于单个对象或室内场景的流行方法不同，或使用缩放轨迹，我们的方法生成具有各种对象的多样场景，甚至扩展到创造想象中的场景。因此，Text2Immersion可广泛应用于各种应用程序，如虚拟现实、游戏开发和自动内容创建。广泛的评估表明，我们的系统在渲染质量和多样性方面超越了其他方法，进一步推动了文本驱动的3D场景生成。