  We introduce HouseCrafter, a novel approach that can lift a floorplan into a
complete large 3D indoor scene (e.g., a house). Our key insight is to adapt a
2D diffusion model, which is trained on web-scale images, to generate
consistent multi-view color (RGB) and depth (D) images across different
locations of the scene. Specifically, the RGB-D images are generated
autoregressively in a batch-wise manner along sampled locations based on the
floorplan, where previously generated images are used as condition to the
diffusion model to produce images at nearby locations. The global floorplan and
attention design in the diffusion model ensures the consistency of the
generated images, from which a 3D scene can be reconstructed. Through extensive
evaluation on the 3D-Front dataset, we demonstrate that HouseCraft can generate
high-quality house-scale 3D scenes. Ablation studies also validate the
effectiveness of different design choices. We will release our code and model
weights. Project page: https://neu-vi.github.io/houseCrafter/
