MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation

Zhiqi Li, Wenhuan Li, Tengfei Wang, Zhenwei Wang, Junta Wu, Haoyuan Wang, Yunhan Yang, Zehuan Huang, YANG LI, Peidong Liu, Chunchao Guo

Keywords: Compositional 3D Generation, Latent Diffusion Models, Sparse Attention

TL;DR: A scalable compositional 3D generation framework for fine-grained part-composed object or instance-composed scene generation.

Abstract: 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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 8609