MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917132973899776 |
|---|---|
| author | Li, Zhiqi Li, Wenhuan Wang, Tengfei Wang, Zhenwei Wu, Junta Wang, Haoyuan Yang, Yunhan Huang, Zehuan Li, Yang Liu, Peidong Guo, Chunchao |
| author_facet | Li, Zhiqi Li, Wenhuan Wang, Tengfei Wang, Zhenwei Wu, Junta Wang, Haoyuan Yang, Yunhan Huang, Zehuan Li, Yang Liu, Peidong Guo, Chunchao |
| contents | Compositionality is critical for 3D object and scene generation, but existing part-aware 3D generation methods suffer from poor scalability due to quadratic global attention costs when increasing the number of components. In this work, we present MoCA, a compositional 3D generative model with two key designs: (1) importance-based component routing that selects top-k relevant components for sparse global attention, and (2) unimportant components compression that preserve contextual priors of unselected components while reducing computational complexity of global attention. With these designs, MoCA enables efficient, fine-grained compositional 3D asset creation with scalable number of components. Extensive experiments show MoCA outperforms baselines on both compositional object and scene generation tasks. Project page: https://lizhiqi49.github.io/MoCA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation Li, Zhiqi Li, Wenhuan Wang, Tengfei Wang, Zhenwei Wu, Junta Wang, Haoyuan Yang, Yunhan Huang, Zehuan Li, Yang Liu, Peidong Guo, Chunchao Computer Vision and Pattern Recognition Compositionality is critical for 3D object and scene generation, but existing part-aware 3D generation methods suffer from poor scalability due to quadratic global attention costs when increasing the number of components. In this work, we present MoCA, a compositional 3D generative model with two key designs: (1) importance-based component routing that selects top-k relevant components for sparse global attention, and (2) unimportant components compression that preserve contextual priors of unselected components while reducing computational complexity of global attention. With these designs, MoCA enables efficient, fine-grained compositional 3D asset creation with scalable number of components. Extensive experiments show MoCA outperforms baselines on both compositional object and scene generation tasks. Project page: https://lizhiqi49.github.io/MoCA |
| title | MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.07628 |