BANG: Dividing 3D Assets via Generative Exploded Dynamics
Authors: name: Longwen Zhang, name: Qixuan Zhang, name: Haoran Jiang, name: Yinuo Bai, name: Wei Yang, name: Lan Xu, name: Jingyi Yu

3D creation has always been a unique human strength, driven by our ability to
deconstruct and reassemble objects using our eyes, mind and hand. However,
current 3D design tools struggle to replicate this natural process, requiring
considerable artistic expertise and manual labor. This paper introduces BANG, a
novel generative approach that bridges 3D generation and reasoning, allowing
for intuitive and flexible part-level decomposition of 3D objects. At the heart
of BANG is "Generative Exploded Dynamics", which creates a smooth sequence of
exploded states for an input geometry, progressively separating parts while
preserving their geometric and semantic coherence.
  BANG utilizes a pre-trained large-scale latent diffusion model, fine-tuned
for exploded dynamics with a lightweight exploded view adapter, allowing
precise control over the decomposition process. It also incorporates a temporal
attention module to ensure smooth transitions and consistency across time. BANG
enhances control with spatial prompts, such as bounding boxes and surface
regions, enabling users to specify which parts to decompose and how. This
interaction can be extended with multimodal models like GPT-4, enabling
2D-to-3D manipulations for more intuitive and creative workflows.
  The capabilities of BANG extend to generating detailed part-level geometry,
associating parts with functional descriptions, and facilitating
component-aware 3D creation and manufacturing workflows. Additionally, BANG
offers applications in 3D printing, where separable parts are generated for
easy printing and reassembly. In essence, BANG enables seamless transformation
from imaginative concepts to detailed 3D assets, offering a new perspective on
creation that resonates with human intuition.