Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields
<|reference_start|>Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields: 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.<|reference_end|>
@article{huang2024part-aware,
    title={Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel
  Fields},
    author={Yuhang Huang and SHilong Zou and Xinwang Liu and Kai Xu},
    journal={arXiv preprint arXiv:2405.00998},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00998},
    primaryClass={cs.CV}
}