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

• URL: http://arxiv.org/abs/2501.09433v1
• Date: Thu, 16 Jan 2025 10:03:15 GMT
• Title: CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
• Authors: Hwan Heo, Jangyeong Kim, Seongyeong Lee, Jeong A Wi, Junyoung Choi, Sangjun Ahn,
• Abstract summary: CaPa is a carve-and-paint framework that generates high-fidelity 3D assets efficiently.<n>It excels in texture fidelity and geometric stability, establishing a new standard for practical, scalable 3D asset generation.
• Score: 2.544527978847722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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