arXiv 2403.051022024-03-08cs.CV

利用高级纹理先验增强高保真纹理生成

Enhancing Texture Generation with High-Fidelity Using Advanced Texture Priors

MetaApp AI Research（MetaApp AI研究院）；Zhengzhou University（郑州大学）；Beijing Institute of Technology（北京理工大学）；Beijing University of Technology（北京工业大学）；Northeastern University（东北大学）

Kuo Xu, Maoyu Wang, Muyu Wang, Lincong Feng, Tianhui Zhang, Xiaoli Liu

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AI总结：

针对现有3D纹理生成在用户简化模型后出现走样模糊、高分辨率合成有噪声且保真度不足的问题，提出结合初始粗糙纹理的高保真纹理修复与自监督背景噪声平滑技术，实验证明其高分辨率纹理恢复效果优于现有方案。

摘要：

近年来2D生成技术的发展引发了关于利用2D先验进行3D形状与纹理内容生成的广泛讨论。然而这类方法往往忽略后续用户操作，例如用户获取3D模型并简化其结构时出现的纹理走样与模糊问题。传统图形学方法可部分缓解该问题，但现有纹理合成技术无法保证与原始模型外观的一致性，也难以实现高保真还原。此外，高分辨率纹理合成中常出现背景噪声，限制了这类生成技术的实际应用。\n本研究提出一种高分辨率高保真纹理修复技术，以粗糙纹理为初始输入，增强合成纹理与初始纹理的一致性，从而解决用户结构简化操作导致的走样与模糊问题。同时我们引入一种基于自监督方案的背景噪声平滑技术，解决当前高分辨率纹理合成方案中的噪声问题。我们的方法可实现高分辨率纹理合成，为高清高细节纹理合成技术铺平了道路。实验表明，在高分辨率条件下的高保真纹理恢复任务中，我们的方案优于现有已知方案。

英文摘要：

The recent advancements in 2D generation technology have sparked a widespread discussion on using 2D priors for 3D shape and texture content generation. However, these methods often overlook the subsequent user operations, such as texture aliasing and blurring that occur when the user acquires the 3D model and simplifies its structure. Traditional graphics methods partially alleviate this issue, but recent texture synthesis technologies fail to ensure consistency with the original model's appearance and cannot achieve high-fidelity restoration. Moreover, background noise frequently arises in high-resolution texture synthesis, limiting the practical application of these generation technologies.In this work, we propose a high-resolution and high-fidelity texture restoration technique that uses the rough texture as the initial input to enhance the consistency between the synthetic texture and the initial texture, thereby overcoming the issues of aliasing and blurring caused by the user's structure simplification operations. Additionally, we introduce a background noise smoothing technique based on a self-supervised scheme to address the noise problem in current high-resolution texture synthesis schemes. Our approach enables high-resolution texture synthesis, paving the way for high-definition and high-detail texture synthesis technology. Experiments demonstrate that our scheme outperforms currently known schemes in high-fidelity texture recovery under high-resolution conditions.

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