Abstract
This paper presents a novel latent 3D diffusion model for the generation of neural voxel fields, aiming to achieve accurate part-aware structures. Compared to existing methods, there are two key designs to ensure high-quality and accurate part-aware generation. On one hand, we introduce a latent 3D diffusion process for neural voxel fields, enabling generation at significantly higher resolutions that can accurately capture rich textural and geometric details. On the other hand, a part-aware shape decoder is introduced to integrate the part codes into the neural voxel fields, guiding the accurate part decomposition and producing high-quality rendering results. Through extensive experimentation and comparisons with state-of-the-art methods, we evaluate our approach across four different classes of data. The results demonstrate the superior generative capabilities of our proposed method in part-aware shape generation, outperforming existing state-of-the-art methods.
Abstract (translated)
本文提出了一种新颖的潜在3D扩散模型,用于生成神经元体素场,旨在实现准确的部分感知结构。与现有方法相比,有两个关键设计可以确保高质量和准确的部分感知生成。一方面,我们引入了一个用于神经元体素场的潜在3D扩散过程,使得生成的分辨率比现有方法更高,能够准确捕捉丰富的纹理和几何细节。另一方面,引入了一个部分感知形状解码器,将部分代码整合到神经元体素场中,指导准确的部分分解并产生高质量渲染结果。通过广泛的实验和与最先进方法的比较,我们评估了我们方法在部分感知形状生成方面的优越性。结果表明,与现有方法相比,我们提出的方法在部分感知形状生成方面的表现更加卓越,超越了现有最先进的方法。
URL
https://arxiv.org/abs/2405.00998
