  The advancement of diffusion models has pushed the boundary of text-to-3D
object generation. While it is straightforward to composite objects into a
scene with reasonable geometry, it is nontrivial to texture such a scene
perfectly due to style inconsistency and occlusions between objects. To tackle
these problems, we propose a coarse-to-fine 3D scene texturing framework,
referred to as RoomTex, to generate high-fidelity and style-consistent textures
for untextured compositional scene meshes. In the coarse stage, RoomTex first
unwraps the scene mesh to a panoramic depth map and leverages ControlNet to
generate a room panorama, which is regarded as the coarse reference to ensure
the global texture consistency. In the fine stage, based on the panoramic image
and perspective depth maps, RoomTex will refine and texture every single object
in the room iteratively along a series of selected camera views, until this
object is completely painted. Moreover, we propose to maintain superior
alignment between RGB and depth spaces via subtle edge detection methods.
Extensive experiments show our method is capable of generating high-quality and
diverse room textures, and more importantly, supporting interactive
fine-grained texture control and flexible scene editing thanks to our
inpainting-based framework and compositional mesh input. Our project page is
available at https://qwang666.github.io/RoomTex/.
