GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fill

📅 2026-08-26
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
该研究通过几何局部自相似学习方法,解决了现有技术在生成全对象纹理时难以保持细节和单视图参考的问题,提供了艺术家可控的纹理生成方案。
📝 Abstract
Using conditional image generators, texture artists can explore many single-view looks for an existing 3D shape. Despite impressive progress, state-of-the-art generative methods still struggle to generate a full object texture while closely adhering to fine scale geometric detail and single view references, leaving little room for artists guidance. Furthermore, current automatic models lack the flexibility for artist to explore multiple textures from varied sources in an interactive and controllable manner. Unlike methods trained on large 3D datasets that generate full object textures from global guidance, our work explores a local and less data-hungry approach to texture with explicit artist control. We leverage the geometric self-similarity and geometry-texture correlation existing in many natural and man-made shapes; and train a shape-specific local texture generation and completion model. This model learns from existing image model priors and a single 3D shape, and is guided by attending to a set of geometry-aware reference patches. The trained shape-specific network can transfer any novel reference to the full target object texture through patchwise inpainting. We show improved or comparable quality to strong image-conditioned texture generation baselines, suggesting local texturing as a promising research direction. Our model also enables local geometry-conditioned texture inpainting, guided by artist-selected references, and generalizes to PBR materials and unseen meshes for texture transfer. We piloted our novel texture fill capability as a Blender addon with several 3D texturing professionals who reported positive feedback on the model's controllability, practical usefulness, and creative affordances.
Problem

Research questions and friction points this paper is trying to address.

texture generation
geometric detail
artist control
conditional image generators
Innovation

Methods, ideas, or system contributions that make the work stand out.

Geometric Local Self-Similarity
Shape-Specific Texture Generation
Reference-Guided Texture Fill
Geometry-Aware Patches
Local Inpainting
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
C
Chenyue Cai
Princeton University, USA
A
Anita Hu
NVIDIA, Canada
James Lucas
James Lucas
Research Scientist, NVIDIA
Machine LearningOptimizationStatistics
Szymon Rusinkiewicz
Szymon Rusinkiewicz
Princeton University, USA
M
Masha Shugrina
NVIDIA, Canada and University of Toronto, Canada