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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on visualization and computer graphics Ročník 31; číslo 10; s. 8057 - 8069
Hlavní autori: Huang, Yuhang, Zou, Shilong, Liu, Xinwang, Xu, Kai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.10.2025
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2025.3562871