PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
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.
