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.

翻译：3D生成模型的兴起使得从多模态输入（如文本或图像）自动合成3D几何与纹理成为可能。然而，这些方法常忽略物理约束与可制造性考量。本研究致力于解决同时满足轻量化与自支撑特性的3D设计生成难题。我们提出DensiCrafter框架，通过优化密度场生成轻量化自支撑3D空心结构。该方法以Trellis生成的粗粒度体素网格为起点，将其解释为连续密度场进行优化，并引入三项可微分、物理约束且无需仿真的损失函数。质量正则化项惩罚冗余材料，受限优化域则保持外表面形态。本方法无需修改架构即可与基于Trellis的预训练模型（如Trellis、DSO）无缝集成。大量实验表明，在文本到3D生成任务中材料质量最高可减少43%。相较于前沿基线方法，本方法在保持高几何保真度的同时显著提升结构稳定性。真实世界3D打印实验证实，所生成空心结构具备可靠可制造性与自支撑能力。