DVD: Discrete Voxel Diffusion for 3D Generation and Editing

Zhengrui Xiang, Jiaqi Wu, Fupeng Sun, Heliang Zheng, Yingzhen Li

Keywords: Generative Models, Deep Learning, Discrete Diffusion Models, 3D Generation

Abstract: We introduce \emph{Discrete Voxel Diffusion} (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for \textsc{SLat} (Structured LATent) based 3D generative pipelines. Although discrete diffusion has not generally displaced continuous diffusion in image-like generation, we show that it can be an effective first-stage prior for sparse voxel scaffolds. By treating voxel occupancy as a native discrete variable, DVD avoids continuous-to-discrete thresholding and provides a simple framework for voxel generation, uncertainty estimation, and editing. Beyond quality gains, DVD provides more interpretable generation dynamics through explicit categorical modeling. Furthermore, we leverage the predictive entropy as a robust uncertainty metric to identify ambiguous voxel regions and complicated samples, facilitating tasks such as data filtering and quality assessment. Finally, we propose a lightweight fine-tuning strategy using block-structured perturbation patterns. This approach empowers the model to inpaint and edit voxels within a single sampling round, requiring negligible auxiliary computation and no additional model evaluations.

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Submission Number: 9