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Scaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion

Created by
  • Haebom

Author

Justin Jung

Outline

To address the challenges of generating sparse, multi-category 3D voxel structures, this paper proposes a novel generative model called Scaffold Diffusion. This method treats voxels as tokens and generates 3D voxel structures using a discrete diffusion language model. Unlike existing autoregressive methods, this model can generate realistic and consistent structures even with data sparsity exceeding 98%. Experimentally, we demonstrate this using Minecraft house structure data from the 3D-Craft dataset. Furthermore, we provide an interactive viewer that visualizes the generated samples and the generation process. Our findings highlight the promise of the discrete diffusion model as a promising framework for generative modeling of 3D sparse voxels.

Takeaways, Limitations

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Takeaways:
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We propose that discrete diffusion language models can be applied to generate spatially consistent 3D structures beyond sequential domains such as text.
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We demonstrate that realistic and consistent 3D voxel structures can be generated even from data with sparsity greater than 98%.
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We present a novel approach to 3D sparse voxel generative modeling.
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Enhances model understanding by providing an interactive viewer that allows you to visualize the generation process.
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Limitations:
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The performance evaluation of the proposed model is limited to the Minecraft house structure dataset. Further research is needed to determine its generalization performance to other types of 3D voxel data.
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There is a lack of analysis of specific computational costs and memory efficiency.
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A more in-depth comparative analysis with other existing 3D generation models is needed.
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