MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment
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arXiv
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| Format: | Preprint |
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2024
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| author | Ceylan, Duygu Deschaintre, Valentin Groueix, Thibault Martin, Rosalie Huang, Chun-Hao Rouffet, Romain Kim, Vladimir Lassagne, Gaëtan |
| author_facet | Ceylan, Duygu Deschaintre, Valentin Groueix, Thibault Martin, Rosalie Huang, Chun-Hao Rouffet, Romain Kim, Vladimir Lassagne, Gaëtan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment Ceylan, Duygu Deschaintre, Valentin Groueix, Thibault Martin, Rosalie Huang, Chun-Hao Rouffet, Romain Kim, Vladimir Lassagne, Gaëtan Computer Vision and Pattern Recognition Graphics 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. |
| title | MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2404.02899 |