PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
This addresses the challenge of efficient and high-quality texture generation for 3D graphics and AI applications, representing an incremental advance over prior multi-view methods.
The paper tackles the problem of generating physically-based rendering (PBR) material textures from 3D meshes and text or image prompts, introducing PacTure to improve global consistency and resolution while reducing inference time, with experiments showing it outperforms state-of-the-art methods in quality and efficiency.
We present PacTure, a novel framework for generating physically-based rendering (PBR) material textures from an untextured 3D mesh, a text description, and an optional image prompt. Early 2D generation-based texturing approaches generate textures sequentially from different views, resulting in long inference times and globally inconsistent textures. More recent approaches adopt multi-view generation with cross-view attention to enhance global consistency, which, however, limits the resolution for each view. In response to these weaknesses, we first introduce view packing, a novel technique that significantly increases the effective resolution for each view during multi-view generation without imposing additional inference cost, by formulating the arrangement of multi-view maps as a 2D rectangle bin packing problem. In contrast to UV mapping, it preserves the spatial proximity essential for image generation and maintains full compatibility with current 2D generative models. To further reduce the inference cost, we enable fine-grained control and multi-domain generation within the next-scale prediction autoregressive framework to create an efficient multi-view multi-domain generative backbone. Extensive experiments show that PacTure outperforms state-of-the-art methods in both quality of generated PBR textures and efficiency in training and inference.