# MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control Source: [MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:42:14.951Z Authors: Mingqi Shao, Feng Xiong, Zhaoxu Sun, Mu Xu Published: 2025-05-19T02:40:24.000Z ## Abstract Recently, significant advances have been made in 3D object generation. Building upon the generated geometry, current pipelines typically employ image diffusion models to generate multi-view RGB images, followed by UV texture reconstruction through texture baking. While 3D geometry generation has improved significantly, supported by multiple open-source frameworks, 3D texture generation remains underexplored. In this work, we systematically investigate 3D texture generation through the lens of three core dimensions: reference-texture alignment, geometry-texture consistency, and local texture quality. To tackle these issues, we propose MVPainter, which employs data filtering and augmentation strategies to enhance texture fidelity and detail, and introduces ControlNet-based geometric conditioning to improve texture-geometry alignment. Furthermore, we extract physically-based rendering (PBR) attributes from the generated views to produce PBR meshes suitable for real-world rendering applications. MVPainter achieves state-of-the-art results across all three dimensions, as demonstrated by human-aligned evaluations. To facilitate further research and reproducibility, we also release our full pipeline as an open-source system, including data construction, model architecture, and evaluation tools. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Emission Map Generation on Objaverse-Emission 500-asset](): 3D Emission Map Generation; Objaverse-Emission 500-asset; Dice Coefficient: 98.749. - [Emission Map Generation on Objaverse-Emission 500-asset](): Emission Map Generation; Objaverse-Emission 500-asset; PSNR: 13.27. - [Albedo Map Generation on Objaverse-Emission 500-asset](): Albedo Map Generation; Objaverse-Emission 500-asset; PSNR: 11.03. - [Material Generation on BlenderVault (test)](): Material generation; BlenderVault (test); CLIP-FID: 6.583. ## Original sources and implementation [Original paper]() [PDF]()