MatAtlas: Text-driven Consistent Geometry Texturing and Material
  Assignment
Duygu Ceylan Valentin Deschaintre Thibault Groueix Rosalie Martin Chun-Hao Huang Romain Rouffet Vladimir Kim Gaëtan Lassagne
We present MatAtlas, a method for consistent text-guided 3D model texturing.
Following recent progress we leverage a large scale text-to-image generation
model (e.g., Stable Diffusion) as a prior to texture a 3D model. We carefully
design an RGB texturing pipeline that leverages a grid pattern diffusion,
driven by depth and edges. By proposing a multi-step texture refinement
process, we significantly improve the quality and 3D consistency of the
texturing output. To further address the problem of baked-in lighting, we move
beyond RGB colors and pursue assigning parametric materials to the assets.
Given the high-quality initial RGB texture, we propose a novel material
retrieval method capitalized on Large Language Models (LLM), enabling
editabiliy and relightability. We evaluate our method on a wide variety of
geometries and show that our method significantly outperform prior arts. We
also analyze the role of each component through a detailed ablation study.