GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation

TL;DR

GenesisTex2 introduces a novel, efficient text-to-texture synthesis framework leveraging pretrained diffusion models, enhancing local detail and cross-view consistency without additional training, outperforming existing methods in quality and speed.

Contribution

The paper presents a new framework that improves texture consistency and detail in text-to-texture generation using local attention reweighing and latent space merging, without extra training.

Findings

Outperforms state-of-the-art in texture consistency and visual quality

Faster than distillation-based methods

Does not require additional training or fine-tuning

Abstract

Large-scale text-guided image diffusion models have shown astonishing results in text-to-image (T2I) generation. However, applying these models to synthesize textures for 3D geometries remains challenging due to the domain gap between 2D images and textures on a 3D surface. Early works that used a projecting-and-inpainting approach managed to preserve generation diversity but often resulted in noticeable artifacts and style inconsistencies. While recent methods have attempted to address these inconsistencies, they often introduce other issues, such as blurring, over-saturation, or over-smoothing. To overcome these challenges, we propose a novel text-to-texture synthesis framework that leverages pretrained diffusion models. We first introduce a local attention reweighing mechanism in the self-attention layers to guide the model in concentrating on spatial-correlated patches across different views, thereby enhancing local details while preserving cross-view consistency. Additionally, we propose a novel latent space merge pipeline, which further ensures consistency across different viewpoints without sacrificing too much diversity. Our method significantly outperforms existing state-of-the-art techniques regarding texture consistency and visual quality, while delivering results much faster than distillation-based methods. Importantly, our framework does not require additional training or fine-tuning, making it highly adaptable to a wide range of models available on public platforms.