Abstract
Diffusion models have shown remarkable results for image generation, editing and inpainting. Recent works explore diffusion models for 3D shape generation with neural implicit functions, i.e., signed distance function and occupancy function. However, they are limited to shapes with closed surfaces, which prevents them from generating diverse 3D real-world contents containing open surfaces. In this work, we present UDiFF, a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally. Our key idea is to generate UDFs in spatial-frequency domain with an optimal wavelet transformation, which produces a compact representation space for UDF generation. Specifically, instead of selecting an appropriate wavelet transformation which requires expensive manual efforts and still leads to large information loss, we propose a data-driven approach to learn the optimal wavelet transformation for UDFs. We evaluate UDiFF to show our advantages by numerical and visual comparisons with the latest methods on widely used benchmarks. Page: this https URL.
Abstract (translated)
扩散模型在图像生成、编辑和修复方面已经取得了显著的成果。最近的工作探索了使用神经隐函数（即距离函数和占有函数）进行3D形状生成的扩散模型。然而，它们仅限于具有封闭表面的形状，这使得它们无法生成包含开放表面的多样3D真实内容。在本文中，我们提出了UDiFF，一种用于无符号距离场（UDFs）的3D扩散模型，可以从文本条件或无条件情况下生成带有开放表面的纹理3D形状。我们的关键想法是在空间-频域中通过最优小波变换生成UDFs，这产生了一个紧凑的UDF生成表示空间。具体来说，我们提出了一个数据驱动的方法来学习最优的小波变换，这需要昂贵的手动努力，并且仍然会导致大量信息损失。我们通过与最新方法在广泛使用的基准上的数值和视觉比较来评估UDiFF，以展示我们的优势。页面链接：https://this URL。
URL
https://arxiv.org/abs/2404.06851
