  Designing high-quality indoor 3D scenes is important in many practical
applications, such as room planning or game development. Conventionally, this
has been a time-consuming process which requires both artistic skill and
familiarity with professional software, making it hardly accessible for layman
users. However, recent advances in generative AI have established solid
foundation for democratizing 3D design. In this paper, we propose a pioneering
approach for text-based 3D room design. Given a prompt in natural language
describing the object placement in the room, our method produces a high-quality
3D scene corresponding to it. With an additional text prompt the users can
change the appearance of the entire scene or of individual objects in it. Built
using in-context learning, CAD model retrieval and 3D-Gaussian-Splatting-based
stylization, our turnkey pipeline produces state-of-the-art 3D scenes, while
being easy to use even for novices. Our project page is available at
https://sceneteller.github.io/.
