CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
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.
6789e73d7a7ce2b07045dd3e
Hwan Heo
claimed_verified
2025-01-20T09:29:05.735Z
65f9520fee1763d31af63615
/avatars/6f7ad9f02a0f19b4452de4dbcafc2715.svg
Hwan Heo
user
hhhwan
6789e73d7a7ce2b07045dd3f
Jangyeong Kim
admin_assigned
2025-01-17T10:48:03.141Z
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/avatars/dd48dff0b639123c605b5c0ee10577d7.svg
Jangyeong.Kim
user
longshiine
6789e73d7a7ce2b07045dd40
Seongyeong Lee
6789e73d7a7ce2b07045dd41
Jeong A Wi
6789e73d7a7ce2b07045dd42
Junyoung Choi
6789e73d7a7ce2b07045dd43
Sangjun Ahn
CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
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.
A two-stage framework, CaPa, uses a 3D latent diffusion model for geometry and a Spatially Decoupled Attention for textures, generating high-quality 3D assets efficiently.
3D latent diffusion model Spatially Decoupled Attention carve-and-paint framework multi-view inputs high-resolution textures 3D-aware occlusion inpainting 3D asset generation