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Proceedings of the AAAI Conference on Artificial Intelligence

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DensiCrafter: Physically-Constrained Generation and Fabri...
Shengqi Dang · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Shengqi Dang Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Innovation Institute Tongji University
  • Fu Chai Shanghai Innovation Institute Tongji University
  • Jiaxin Li Shanghai Innovation Institute
  • Chao Yuan Shanghai Research Institute for Intelligent Autonomous Systems Tongji University
  • Wei Ye Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Innovation Institute Tongji University
  • Nan Cao Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Innovation Institute Tongji University

DOI:

https://doi.org/10.1609/aaai.v40i1.36979

Abstract

The rise of 3D generative models has enabled automatic 3D geometry and texture synthesis from multimodal inputs (e.g., text or images). However, these methods often ignore physical constraints and manufacturability considerations. In this work, we address the challenge of producing 3D designs that are both lightweight and self-supporting. We present DensiCrafter, a framework for generating lightweight, self-supporting 3D hollow structures by optimizing the density field. Starting from coarse voxel grids produced by Trellis, we interpret these as continuous density fields to optimize and introduce three differentiable, physically constrained, and simulation-free loss terms. Additionally, a mass regularization penalizes unnecessary material, while a restricted optimization domain preserves the outer surface. Our method seamlessly integrates with pretrained Trellis-based models (e.g., Trellis, DSO) without any architectural changes. In extensive evaluations, we achieve up to 43% reduction in material mass on the text-to-3D task. Compared to state-of-the-art baselines, our method could improve the stability and maintain high geometric fidelity. Real-world 3D-printing experiments confirm that our hollow designs can be reliably fabricated and could be self-supporting.

How to Cite

Dang, S., Chai, F., Li, J., Yuan, C., Ye, W., & Cao, N. (2026). DensiCrafter: Physically-Constrained Generation and Fabrication of Self-Supporting Hollow Structures. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 193–201. https://doi.org/10.1609/aaai.v40i1.36979

Issue

Section

AAAI Technical Track on Application Domains I

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