Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields

• URL: http://arxiv.org/abs/2405.00998v3
• Date: Thu, 20 Jun 2024 08:49:50 GMT
• Title: Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields
• Authors: Yuhang Huang, SHilong Zou, Xinwang Liu, Kai Xu,
• Abstract summary: We introduce a latent 3D diffusion process for neural voxel fields, enabling generation at significantly higher resolutions. A part-aware shape decoder is introduced to integrate the part codes into the neural voxel fields, guiding the accurate part decomposition. The results demonstrate the superior generative capabilities of our proposed method in part-aware shape generation, outperforming existing state-of-the-art methods.
• Score: 50.12118098874321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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