Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation

Recent breakthroughs in 3D generation have advanced no-tably with the development of text-to-image diffusion model. However,existing methods remain two practical challenges: (1) They primarilygenerate single 3D object, but struggle to generate multi-object compo-sitional 3D assets due to the lack of the modeling for Gaussian prim-itives in reasonable interactions. (2) They often suffer from cross-viewinconsistency during 3D optimization, as Score Distillation Samplinginherently performs on each single view, inevitably resulting in cross-view hallucinations. To solve above issues, we propose I2 C-3D, a noveloptimization-based method to generate multi-view consistent composi-tional 3D assets with reasonable interactions. Specifically, we proposean Inclusive Interactive Collisions strategy to guide Gaussian primitivesappearing in reasonable interaction regions naturally, thereby ensuringobjects in the compositional scene interact in a physically plausible andvisually coherent way. Additionally, to enhance multi-view consistency,Multi-View Adaptive Score Distillation Sampling is devised to distillmulti-view consistency prior and layout prior from pre-trained diffusionmodel by modulating attention map of instance token and spatial to-ken across viewpoints. Benefiting from above elaborate designs, I2 C-3Dnot only generates high-fidelity multi-view consistent compositional 3Dassets but also supports 3D editing flexibly, facilitating complex scenegeneration. Extensive experiments demonstrate our I2 C-3D outperformsexisting methods in generation quality and multi-view consistency.