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.

翻译：本文提出一种新颖的潜在三维扩散模型，用于生成神经体素场，旨在实现精确的部件感知结构。与现有方法相比，本文有两个关键设计以确保高质量且精确的部件感知生成。一方面，我们针对神经体素场引入潜在三维扩散过程，使得在显著更高分辨率下的生成成为可能，从而能够精确捕捉丰富的纹理和几何细节。另一方面，本文引入部件感知形状解码器，将部件编码集成到神经体素场中，指导精确的部件分解并产生高质量的渲染结果。通过大量实验并与最先进的方法进行比较，我们在四类不同数据上评估了我们的方法。结果表明，我们提出的方法在部件感知形状生成方面具有优越的生成能力，超越了现有的最先进方法。