Efficient Creation of Textured 4K Meshes with CaPa: A New Approach in 3D Generation

The generation of high-quality 3D models from text or visual input is a central theme in modern generative modeling. Despite continuous progress, existing 3D generation algorithms often struggle with challenges such as inconsistencies in multi-view representation, long generation times, low detail fidelity, and problems with surface reconstruction. CaPa, a novel "Carve-and-Paint" framework, promises to overcome these hurdles and efficiently create high-quality 3D assets.

Two-Phase Process for Geometry and Texture

CaPa decouples geometry generation from texture synthesis in a two-stage process. First, a latent 3D diffusion model generates the geometry based on multi-view input. This approach ensures the structural consistency of the model from different viewpoints. In the second step, the framework synthesizes high-resolution textures (up to 4K) for the generated geometry. This utilizes a novel, model-agnostic, spatially decoupled attention mechanism.

Spatially Decoupled Attention and 3D-Aware Occlusion Inpainting

The spatially decoupled attention allows for the efficient synthesis of high-resolution textures by reducing computational cost while maximizing detail fidelity. In addition, CaPa uses a 3D-aware occlusion inpainting algorithm. This algorithm fills untextured areas and ensures a seamless result across the entire model. Through this combination of techniques, CaPa achieves high quality and speed in 3D asset generation.

Fast Generation and Application Potential

The entire CaPa pipeline generates high-quality 3D assets in less than 30 seconds. This speed makes CaPa an attractive solution for commercial applications that require 3D models quickly and efficiently. Potential applications range from games and movies to VR and AR applications. The high resolution of the textures and the geometric stability of the models open up new possibilities for realistic and immersive experiences.

Experimental Results and Outlook

Initial experimental results show that CaPa is convincing in terms of both texture fidelity and geometric stability. The framework sets a new standard for the practical and scalable generation of 3D assets. Future research could focus on expanding the capabilities of CaPa, for example, by integrating semantic information or supporting more complex geometries. The efficient generation of 4K textures in a short time positions CaPa as a promising technology for the future of 3D modeling.