  3D Content Generation is at the heart of many computer graphics applications,
including video gaming, film-making, virtual and augmented reality, etc. This
paper proposes a novel deep-learning based approach for automatically
generating interactive and playable 3D game scenes, all from the user's casual
prompts such as a hand-drawn sketch. Sketch-based input offers a natural, and
convenient way to convey the user's design intention in the content creation
process. To circumvent the data-deficient challenge in learning (i.e. the lack
of large training data of 3D scenes), our method leverages a pre-trained 2D
denoising diffusion model to generate a 2D image of the scene as the conceptual
guidance. In this process, we adopt the isometric projection mode to factor out
unknown camera poses while obtaining the scene layout. From the generated
isometric image, we use a pre-trained image understanding method to segment the
image into meaningful parts, such as off-ground objects, trees, and buildings,
and extract the 2D scene layout. These segments and layouts are subsequently
fed into a procedural content generation (PCG) engine, such as a 3D video game
engine like Unity or Unreal, to create the 3D scene. The resulting 3D scene can
be seamlessly integrated into a game development environment and is readily
playable. Extensive tests demonstrate that our method can efficiently generate
high-quality and interactive 3D game scenes with layouts that closely follow
the user's intention.
