CaPa：用于高效生成4K纹理网格的雕刻与绘制综合技术

摘要

从文本或视觉输入中合成高质量的3D资产已成为现代生成建模中的核心目标。尽管3D生成算法层出不穷，但它们经常面临诸如多视角不一致、生成时间缓慢、低保真度和表面重建问题等挑战。虽然一些研究已经解决了其中一些问题，但一个全面的解决方案仍然难以实现。在本文中，我们介绍了CaPa，一个雕刻和绘制框架，可以高效生成高保真度的3D资产。CaPa采用两阶段过程，将几何生成与纹理合成分离开来。首先，一个3D潜扩散模型根据多视角输入生成几何，确保在各个视角之间结构一致性。随后，利用一种新颖的、与模型无关的空间分离注意力机制，该框架为给定几何合成高分辨率纹理（高达4K）。此外，我们提出了一种3D感知的遮挡修补算法，填补未纹理化的区域，从而使整个模型呈现出连贯的结果。这一流程在不到30秒内生成高质量的3D资产，为商业应用提供即用输出。实验结果表明，CaPa在纹理保真度和几何稳定性方面表现出色，为实用、可扩展的3D资产生成建立了新的标准。

English

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 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.

CaPa：用于高效生成4K纹理网格的雕刻与绘制综合技术

CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation

摘要

Summary

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