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About this paper
Lihe Ding, Shaocong Dong, Yaokun Li, Chenjian Gao, Xiao Chen, Rui Han, Yihao Kuang, Hong Zhang and 5 more
FullPart introduces a hierarchical 3D generation framework that produces high-resolution, semantically meaningful parts by combining implicit layout generation with explicit, dedicated full-resolution voxel spaces for each component. The framework achieves superior geometric fidelity and global coherence by resolving the resolution bottleneck in prior methods and includes the creation of PartVerse-XL, a large-scale human-annotated dataset.
Key insights
- A hybrid approach that leverages implicit representations for abstract layout generation and explicit full-resolution representations for individual part geometry can effectively overcome limitations of existing 3D part generation methods.
- Allocating an isolated, full-resolution space to each part is crucial for capturing intricate details, ensuring small parts are not under-represented in a shared, coarser grid.
- Explicitly encoding global spatial context, such as center and corner coordinates, is vital for maintaining accurate spatial relationships and global coherence between parts generated in their own normalized coordinate systems.
- High-quality, human-curated part-level training data is indispensable for training robust models that generate semantically correct and structurally coherent 3D parts.
Summary by alphaXiv.
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