Li et al.
Large-scale dataset and token-fusion architecture improve 3D asset editing
Alchemy3D provides 1.38 million editing pairs across seven types and a benchmark, outperforming prior methods on fidelity and source preservation.
Top University
Badi Li · Tianxin Huang · Yu Zhou · Wei-Shi Zheng · Yi Ma · Shenghua Gao
The University of Hong Kong · Shenzhen Loop Area Institute · Shanghai Innovation Institute · Sun Yat-Sen University
Research Digest··2 min read
The authors present Alchemy3D, a unified framework for 3D asset editing that includes a million-scale dataset, a token-fusion model architecture, and a new benchmark.
Why this paper
From The University of Hong Kong and 3 others
In one line
Alchemy3D achieves better 3D asset editing fidelity, source preservation, and visual quality than prior methods.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
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