Part-Aware Shape Generation With Latent 3D Diffusion of Neural Voxel Fields

This article introduces a novel latent 3D diffusion model for generating neural voxel fields with precise part-aware structures and high-quality textures. In comparison to existing methods, this approach incorporates two key designs to guarantee high-quality and accurate part-aware generation. On on...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 31; no. 10; pp. 8057 - 8069
Main Authors: Huang, Yuhang, Zou, Shilong, Liu, Xinwang, Xu, Kai
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2025
Subjects:
3D diffusion models
Codes
Decoding
Diffusion models
Diffusion processes
Image color analysis
Noise reduction
part-aware generation
Rendering (computer graphics)
Shape
shape generation
Three-dimensional displays
Training
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary:This article introduces a novel latent 3D diffusion model for generating neural voxel fields with precise part-aware structures and high-quality textures. In comparison to existing methods, this approach incorporates two key designs to guarantee high-quality and accurate part-aware generation. On one hand, we introduce a latent 3D diffusion process for neural voxel fields, incorporating part-aware information into the diffusion process and allowing generation at significantly higher resolutions to capture rich textural and geometric details accurately. On the other hand, a part-aware shape decoder is introduced to integrate the part codes into the neural voxel fields, guiding accurate part decomposition and producing high-quality rendering results. Importantly, part-aware learning establishes structural relationships to generate texture information for similar regions, thereby facilitating high-quality rendering results. We evaluate our approach across eight different data classes through extensive experimentation and comparisons with state-of-the-art methods. The results demonstrate that our proposed method has superior generative capabilities in part-aware shape generation, outperforming existing state-of-the-art methods. Moreover, we have conducted image- and text-guided shape generation via the conditioned diffusion process, showcasing the advanced potential in multi-modal guided shape generation.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2025.3562871