用两阶段生成高质量室内风格纹理，支持文本和图像提示。
InsTex: Indoor Scenes Stylized Texture Synthesis
- 先用深度图生成多视角图像，再细化纹理保证一致性。
- 在多个数据集上视觉质量优于现有方法，支持跨视角风格统一。
- 适合室内设计、游戏和虚拟现实等需要高保真纹理的应用。
为3D场景生成高质量纹理对室内设计、游戏及增强/虚拟现实（AR/VR）应用至关重要。尽管近期3D生成模型提升了内容创作能力，但在广泛泛化性和多视角风格一致性方面仍面临挑战。现有方法如适配3D文本的2D扩散模型存在处理时间长、视觉伪影等问题，而基于3D数据的方法则泛化能力不足。为此，我们提出InsTex，一种两阶段架构，用于生成高质量、风格一致的3D室内场景纹理。InsTex采用从粗到精的流程，利用预训练2D扩散模型的深度到图像先验生成多视角图像，并进一步优化纹理以实现一致性。该方法支持文本与视觉提示，在视觉质量与定量指标上达到当前最优表现，且在多种3D纹理应用中展现出有效性。
原文摘要 · Abstract (English)
Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR). Although recent advancements in 3D generative models have enhanced content creation, significant challenges remain in achieving broad generalization and maintaining style consistency across multiple viewpoints. Current methods, such as 2D diffusion models adapted for 3D texturing, suffer from lengthy processing times and visual artifacts, while approaches driven by 3D data often fail to generalize effectively. To overcome these challenges, we introduce InsTex, a two-stage architecture designed to generate high-quality, style-consistent textures for 3D indoor scenes. InsTex utilizes depth-to-image diffusion priors in a coarse-to-fine pipeline, first generating multi-view images with a pre-trained 2D diffusion model and subsequently refining the textures for consistency. Our method supports both textual and visual prompts, achieving state-of-the-art results in visual quality and quantitative metrics, and demonstrates its effectiveness across various 3D texturing applications.
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