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

TL;DR
BANG introduces a generative method for intuitive, part-level decomposition of 3D objects using a diffusion model, enabling smooth exploded views, detailed geometry, and enhanced control for creative and manufacturing applications.
Contribution
The paper presents BANG, a novel approach that combines generative diffusion models with spatial prompts and multimodal integration for flexible 3D part decomposition.
Findings
Creates smooth exploded sequences of 3D objects.
Enables precise control over part decomposition.
Facilitates applications in 3D printing and design workflows.
Abstract
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…
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