Text-Guided Texturing by Synchronized Multi-View Diffusion
Yuxin Liu Minshan Xie Hanyuan Liu Tien-Tsin Wong
This paper introduces a novel approach to synthesize texture to dress up a
given 3D object, given a text prompt. Based on the pretrained text-to-image
(T2I) diffusion model, existing methods usually employ a project-and-inpaint
approach, in which a view of the given object is first generated and warped to
another view for inpainting. But it tends to generate inconsistent texture due
to the asynchronous diffusion of multiple views. We believe such asynchronous
diffusion and insufficient information sharing among views are the root causes
of the inconsistent artifact. In this paper, we propose a synchronized
multi-view diffusion approach that allows the diffusion processes from
different views to reach a consensus of the generated content early in the
process, and hence ensures the texture consistency. To synchronize the
diffusion, we share the denoised content among different views in each
denoising step, specifically blending the latent content in the texture domain
from views with overlap. Our method demonstrates superior performance in
generating consistent, seamless, highly detailed textures, comparing to
state-of-the-art methods.