Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models. However, abstract textual prompts are limited in providing global textural or shape information, which results in the texture generation methods producing blurry or inconsistent patterns. To tackle this, we present FlexiTex, embedding rich information via visual guidance to generate a high-quality texture. The core of FlexiTex is the Visual Guidance Enhancement module, which incorporates more specific information from visual guidance to reduce ambiguity in the text prompt and preserve high-frequency details. To further enhance the visual guidance, we introduce a Direction-Aware Adaptation module that automatically designs direction prompts based on different camera poses, avoiding the Janus problem and maintaining semantically global consistency. Benefiting from the visual guidance, FlexiTex produces quantitatively and qualitatively sound results, demonstrating its potential to advance texture generation for real-world applications.

翻译：近期纹理生成方法因利用了大规模文本到图像扩散模型的强大生成先验而取得了令人瞩目的成果。然而，抽象的文本提示在提供全局纹理或形状信息方面存在局限，导致纹理生成方法产生模糊或不一致的图案。为解决此问题，我们提出了FlexiTex，通过视觉引导嵌入丰富信息以生成高质量纹理。FlexiTex的核心是视觉引导增强模块，该模块整合来自视觉引导的更具体信息，以减少文本提示的模糊性并保留高频细节。为进一步增强视觉引导，我们引入了方向感知适应模块，该模块基于不同相机姿态自动设计方向提示，避免Janus问题并保持语义上的全局一致性。得益于视觉引导，FlexiTex在定量和定性上均产生了良好的结果，展示了其在推动现实应用纹理生成方面的潜力。