  We introduce RealmDreamer, a technique for generation of general
forward-facing 3D scenes from text descriptions. Our technique optimizes a 3D
Gaussian Splatting representation to match complex text prompts. We initialize
these splats by utilizing the state-of-the-art text-to-image generators,
lifting their samples into 3D, and computing the occlusion volume. We then
optimize this representation across multiple views as a 3D inpainting task with
image-conditional diffusion models. To learn correct geometric structure, we
incorporate a depth diffusion model by conditioning on the samples from the
inpainting model, giving rich geometric structure. Finally, we finetune the
model using sharpened samples from image generators. Notably, our technique
does not require video or multi-view data and can synthesize a variety of
high-quality 3D scenes in different styles, consisting of multiple objects. Its
generality additionally allows 3D synthesis from a single image.
