BANG: Dividing 3D Assets via Generative Exploded Dynamics
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.
688ad3c18b724c8c7187dd67
Longwen Zhang
claimed_verified
2025-07-31T08:15:02.207Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1679399015950-636d12455aaed143cd665607.png
ZLW
user
ZarkLngeW
688ad3c18b724c8c7187dd68
Qixuan Zhang
688ad3c18b724c8c7187dd69
Haoran Jiang
688ad3c18b724c8c7187dd6a
Yinuo Bai
688ad3c18b724c8c7187dd6b
Wei Yang
688ad3c18b724c8c7187dd6c
Lan Xu
688ad3c18b724c8c7187dd6d
Jingyi Yu
BANG: Dividing 3D Assets via Generative Exploded Dynamics
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.
BANG is a generative approach using latent diffusion models and temporal attention to enable intuitive, part-level decomposition of 3D objects with precise control and multimodal interaction.
latent diffusion model exploded dynamics exploded view adapter temporal attention module spatial prompts GPT-4 component-aware 3D creation 3D printing