CaPa：用于高效生成4K带纹理网格的Carve-n-Paint合成方法
CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
AI总结：
针对现有3D生成算法多视角不一致、速度慢、保真度低等问题，提出两阶段CaPa框架，解耦几何生成与纹理合成，结合空间解耦注意力和3D感知遮挡修复，可30秒内生成高保真4K带纹理3D资产，性能优于现有方法。
摘要：
从文本或视觉输入合成高质量3D资产已成为现代生成式建模的核心目标。尽管3D生成算法不断涌现，但它们常面临多视角不一致、生成速度慢、保真度低以及表面重建等难题。已有研究虽解决了部分问题，但仍缺乏全面的解决方案。本文提出\textbf{CaPa}——一种“雕刻-绘制”框架，可高效生成高保真3D资产。CaPa采用两阶段流程，将几何生成与纹理合成解耦：首先，3D潜扩散模型在多视角输入引导下生成几何结构，确保跨视角结构一致性；随后，该框架借助一种全新的、模型无关的空间解耦注意力（Spatially Decoupled Attention），为给定几何结构合成最高达4K的高分辨率纹理。此外，我们提出一种3D感知遮挡修复算法，用于填充无纹理区域，使整个模型的纹理效果连贯统一。该流水线可在30秒内生成高质量3D资产，输出结果可直接用于商业应用。实验结果表明，CaPa在纹理保真度和几何稳定性方面均表现优异，为实用、可扩展的3D资产生成树立了新标杆。
英文摘要：
The synthesis of high-quality 3D assets from textual or visual inputs has become a central objective in modern generative modeling. Despite the proliferation of 3D generation algorithms, they frequently grapple with challenges such as multi-view inconsistency, slow generation times, low fidelity, and surface reconstruction problems. While some studies have addressed some of these issues, a comprehensive solution remains elusive. In this paper, we introduce \textbf{CaPa}, a carve-and-paint framework that generates high-fidelity 3D assets efficiently. CaPa employs a two-stage process, decoupling geometry generation from texture synthesis. Initially, a 3D latent diffusion model generates geometry guided by multi-view inputs, ensuring structural consistency across perspectives. Subsequently, leveraging a novel, model-agnostic Spatially Decoupled Attention, the framework synthesizes high-resolution textures (up to 4K) for a given geometry. Furthermore, we propose a 3D-aware occlusion inpainting algorithm that fills untextured regions, resulting in cohesive results across the entire model. This pipeline generates high-quality 3D assets in less than 30 seconds, providing ready-to-use outputs for commercial applications. Experimental results demonstrate that CaPa excels in both texture fidelity and geometric stability, establishing a new standard for practical, scalable 3D asset generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
