基于图像生成可编辑的3D物体，支持任意数量的完整部件。
Efficient Part-level 3D Object Generation via Dual Volume Packing
- 采用双体积填充策略，将部件分置于互补体积中组装
- 生成物体部件完整且语义明确，质量与多样性优于现有方法
- 适合需要精细编辑和个性化定制3D模型的研究者
近年来，3D物体生成技术在质量和效率上取得显著进展。然而，大多数方法生成的是所有部件融合在一起的单一网格，限制了对单个部件的编辑与操作能力。主要挑战在于不同物体可能包含不同数量的部件。为此，我们提出一种端到端的部件级3D物体生成框架。给定一张输入图像，该方法可生成具有任意数量完整且语义明确部件的高质量3D物体。我们引入双体积填充策略，将所有部件组织进两个互补体积中，实现完整且交错排列的部件组装。实验表明，本模型在质量、多样性和泛化能力上均优于现有的基于图像的部件级生成方法。
原文摘要 · Abstract (English)
Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts. A key challenge is that different objects may have a varying number of parts. To address this, we propose a new end-to-end framework for part-level 3D object generation. Given a single input image, our method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts. We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. Experiments show that our model achieves better quality, diversity, and generalization than previous image-based part-level generation methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。