2024/04/03 by Duygu Ceylan, Ceylan, Duygu, Valentin Deschaintre +13
Computer Science · Engineering · #Additive Manufacturing and 3D Printing Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR) #Interactive and Immersive Displays #Modular Robots and Swarm Intelligence
paper · pdf · doi:10.48550/arxiv.2404.02899
openalex publication_date 2024/04/03 · openalex created_date 2024/04/06 · openalex updated_date 2026/07/28
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.