CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
Authors: name: Hwan Heo, name: Jangyeong Kim, name: Seongyeong Lee, name: Jeong A Wi, name: Junyoung Choi, name: Sangjun Ahn

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.