DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture
  Propagation
Diffusion-based methods have achieved prominent success in generating 2D
media. However, accomplishing similar proficiencies for scene-level mesh
texturing in 3D spatial applications, e.g., XR/VR, remains constrained,
primarily due to the intricate nature of 3D geometry and the necessity for
immersive free-viewpoint rendering. In this paper, we propose a novel indoor
scene texturing framework, which delivers text-driven texture generation with
enchanting details and authentic spatial coherence. The key insight is to first
imagine a stylized 360{\deg} panoramic texture from the central viewpoint of
the scene, and then propagate it to the rest areas with inpainting and
imitating techniques. To ensure meaningful and aligned textures to the scene,
we develop a novel coarse-to-fine panoramic texture generation approach with
dual texture alignment, which both considers the geometry and texture cues of
the captured scenes. To survive from cluttered geometries during texture
propagation, we design a separated strategy, which conducts texture inpainting
in confidential regions and then learns an implicit imitating network to
synthesize textures in occluded and tiny structural areas. Extensive
experiments and the immersive VR application on real-world indoor scenes
demonstrate the high quality of the generated textures and the engaging
experience on VR headsets. Project webpage:
https://ybbbbt.com/publication/dreamspace
6535f496ae42162a1e110ea1
Bangbang Yang
claimed_verified
2023-10-23T09:06:23.653Z
63d748ff6f49aa82306b7e48
/avatars/9f0b8b8a09b14d76e52ed1bd312e6b63.svg
BB Yang
user
ybbbbt
6535f496ae42162a1e110ea2
Wenqi Dong
6535f496ae42162a1e110ea3
Lin Ma
6535f496ae42162a1e110ea4
Wenbo Hu
claimed_verified
2024-09-04T12:00:55.558Z
657a7458afbb0117ba15c59f
https://cdn-avatars.huggingface.co/v1/production/uploads/657a7458afbb0117ba15c59f/8_iwTS1UG_mKnfylFbLsY.jpeg
Wenbo Hu
user
wbhu-tc
6535f496ae42162a1e110ea5
Xiao Liu
6535f496ae42162a1e110ea6
Zhaopeng Cui
6535f496ae42162a1e110ea7
Yuewen Ma
DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture
  Propagation
Diffusion-based methods have achieved prominent success in generating 2D
media. However, accomplishing similar proficiencies for scene-level mesh
texturing in 3D spatial applications, e.g., XR/VR, remains constrained,
primarily due to the intricate nature of 3D geometry and the necessity for
immersive free-viewpoint rendering. In this paper, we propose a novel indoor
scene texturing framework, which delivers text-driven texture generation with
enchanting details and authentic spatial coherence. The key insight is to first
imagine a stylized 360{\deg} panoramic texture from the central viewpoint of
the scene, and then propagate it to the rest areas with inpainting and
imitating techniques. To ensure meaningful and aligned textures to the scene,
we develop a novel coarse-to-fine panoramic texture generation approach with
dual texture alignment, which both considers the geometry and texture cues of
the captured scenes. To survive from cluttered geometries during texture
propagation, we design a separated strategy, which conducts texture inpainting
in confidential regions and then learns an implicit imitating network to
synthesize textures in occluded and tiny structural areas. Extensive
experiments and the immersive VR application on real-world indoor scenes
demonstrate the high quality of the generated textures and the engaging
experience on VR headsets. Project webpage:
https://ybbbbt.com/publication/dreamspace
A novel indoor scene texturing framework uses diffusion-based methods for text-driven texture generation with high detail and spatial coherence, addressing challenges in 3D spatial applications like XR/VR through dual texture alignment and a separated inpainting strategy.
diffusion-based methods scene-level mesh texturing 3D spatial applications scene texturing framework stylized 360{\deg} panoramic texture inpainting techniques dual texture alignment texture inpainting implicit imitating network real-world indoor scenes VR application