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MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D

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arxiv 2411.02336 v1 pith:JD2HQY2H submitted 2024-11-04 cs.CV

classification cs.CV
keywords mvpainttexturingmulti-viewresultsbenchmarkconsistencydatasetdiscontinuities
verification ladder T0 review T1 audit T2 compute T3 formal
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Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and their heavy dependence on UV unwrapping outcomes. To tackle these challenges, we propose a novel generation-refinement 3D texturing framework called MVPaint, which can generate high-resolution, seamless textures while emphasizing multi-view consistency. MVPaint mainly consists of three key modules. 1) Synchronized Multi-view Generation (SMG). Given a 3D mesh model, MVPaint first simultaneously generates multi-view images by employing an SMG model, which leads to coarse texturing results with unpainted parts due to missing observations. 2) Spatial-aware 3D Inpainting (S3I). To ensure complete 3D texturing, we introduce the S3I method, specifically designed to effectively texture previously unobserved areas. 3) UV Refinement (UVR). Furthermore, MVPaint employs a UVR module to improve the texture quality in the UV space, which first performs a UV-space Super-Resolution, followed by a Spatial-aware Seam-Smoothing algorithm for revising spatial texturing discontinuities caused by UV unwrapping. Moreover, we establish two T2T evaluation benchmarks: the Objaverse T2T benchmark and the GSO T2T benchmark, based on selected high-quality 3D meshes from the Objaverse dataset and the entire GSO dataset, respectively. Extensive experimental results demonstrate that MVPaint surpasses existing state-of-the-art methods. Notably, MVPaint could generate high-fidelity textures with minimal Janus issues and highly enhanced cross-view consistency.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FlexPainter: Flexible and Multi-View Consistent Texture Generation

    cs.GR 2025-06 conditional novelty 6.0 of 10

    FlexPainter combines multi-view grid generation, UV-space view synchronization with a learned weighting network, and multi-modal embedding control to generate consistent, high-resolution textures from text and image prompts.

  2. UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

    cs.CV 2025-05 conditional novelty 5.0 of 10

    UniTEX generates textures for 3D shapes by predicting continuous volumetric texture functions, bypassing UV maps.

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