CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation (2501.09433v1)

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.

• The paper presents CaPa, a novel two-stage framework that separates 3D geometry generation from high-resolution texture synthesis for efficient asset creation.
• CaPa employs multi-view guided 3D latent diffusion and a new Spatially Decoupled Attention mechanism to generate consistent 4K textures from multi-view inputs.
• This method dramatically reduces generation time to under 30 seconds while incorporating 3D-aware occlusion inpainting to fill untextured regions and enhance fidelity.

Overview of CaPa Framework for High-Fidelity 3D Mesh Generation

The paper presents an advanced method for generating high-quality 3D assets, titled "CaPa: Carve-and-Paint Synthesis for Efficient 4K Textured Mesh Generation." This methodology addresses the challenges inherent in 3D content synthesis, such as multi-view inconsistency, slow generation times, and surface reconstruction problems. The authors propose a novel framework that decouples the 3D geometry generation from texture synthesis, producing high-fidelity 3D assets in less than 30 seconds.

The CaPa approach involves a two-stage process. Initially, geometry is generated using a 3D latent diffusion model under the guidance of multi-view inputs to ensure structural consistency across perspectives. In the subsequent stage, textures are synthesized using a novel Spatially Decoupled Attention mechanism, achieving high-resolution textures of up to 4K quality. An additional highlight of this work is the introduction of a 3D-aware occlusion inpainting algorithm that effectively fills untextured regions, enhancing overall model cohesion.

The numerical evaluations reported in the paper underscore significant improvements in texture fidelity and geometric stability compared to existing methods, thus providing a new standard for the practical and scalable generation of 3D assets.

Methodological Insights and Contributions

1. Separation of Geometry and Texture Generation: The CaPa framework introduces a separation between geometry generation and texture synthesis. By doing so, the model improves the flexibility and performance of each stage. This separation allows for precise geometric reconstruction and detailed texture outputs, reducing dependencies and improving task-specific optimizations.
2. Multi-View Guided 3D Latent Diffusion: For geometry creation, CaPa employs a 3D latent diffusion model, enhancing guidance through multi-view inputs. This ensures that the synthesized 3D structure remains consistent across different viewpoints, mitigating problems like the Janus artifact.
3. Spatially Decoupled Attention: The introduction of the Spatially Decoupled Attention mechanism is pivotal in resolving multi-view inconsistencies. This approach does not require architectural modifications or comprehensive retraining, thereby integrating smoothly with large generative models like SDXL, significantly boosting textural fidelity.
4. 3D-Aware Occlusion Inpainting: The proposed occlusion inpainting efficiently addresses the challenge of untextured regions by generating a UV map that preserves surface locality. This algorithm minimizes visible seams and enhances texture continuity when mapped onto 3D surfaces.

Practical and Theoretical Implications

The CaPa method's application potential spans various commercial domains, including gaming, film, and VR/AR, where the demand for high-quality, scalable 3D assets is growing. By reducing the generation time to less than 30 seconds, CaPa significantly enhances efficiency for industries reliant on rapid asset prototyping and deployment. The framework's compatibility with large-scale generative models points to broader implications for integrating AI-driven models with existing 3D rendering and animation pipelines.

Theoretically, the paper provides substantial advancements in understanding multi-view synchronization and its application to texture consistency in 3D environments. The Spatially Decoupled Attention model sets a precedent for future work aiming to optimize multi-view data integration in generative networks.

Future Directions

While CaPa achieves significant improvements in efficiency and quality, future research could explore deeper integration of physically based rendering (PBR) techniques to enhance material realism and surface reflectance properties. Furthermore, extending the current framework to accommodate interactive design adjustments could pave the way for adaptive 3D modeling tools capable of real-time feedback. Investigating the applicability of the framework in dynamic environments, particularly those involving real-time adjustments, could further expand its utility in complex simulations and interactive applications.