title: CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
summary: 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.
paper_authors._id: 6789e73d7a7ce2b07045dd3e
paper_authors.name: Hwan Heo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T09:29:05.735Z
paper_authors._id: 65f9520fee1763d31af63615
paper_authors.avatarUrl: /avatars/6f7ad9f02a0f19b4452de4dbcafc2715.svg
paper_authors.fullname: Hwan Heo
paper_authors.type: user
paper_authors.user: hhhwan
paper_authors._id: 6789e73d7a7ce2b07045dd3f
paper_authors.name: Jangyeong Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:48:03.141Z
paper_authors._id: 634c1f9bb6628cbe2861dcc2
paper_authors.avatarUrl: /avatars/dd48dff0b639123c605b5c0ee10577d7.svg
paper_authors.fullname: Jangyeong.Kim
paper_authors.type: user
paper_authors.user: longshiine
paper_authors._id: 6789e73d7a7ce2b07045dd40
paper_authors.name: Seongyeong Lee
paper_authors._id: 6789e73d7a7ce2b07045dd41
paper_authors.name: Jeong A Wi
paper_authors._id: 6789e73d7a7ce2b07045dd42
paper_authors.name: Junyoung Choi
paper_authors._id: 6789e73d7a7ce2b07045dd43
paper_authors.name: Sangjun Ahn
paper_title: CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
paper_summary: 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.
paper_ai_keywords: 3D assets generative modeling multi-view inconsistency slow generation times low fidelity surface reconstruction CaPa carve-and-paint framework 3D latent diffusion model geometry generation multi-view inputs structural consistency Spatially Decoupled Attention texture synthesis high-resolution textures 4K 3D-aware occlusion inpainting iterative model cohesive results geometric stability practical, scalable 3D asset generation