3D生成从整件升级到零件级
现在的AI生成3D模型，一次只能吐出一个焊死的整体。你想拆开它的胳膊、单独换个轮子？做不到。这篇把生成器从「整体」升级到「零件」:用双体积表示法解决两个零件接触面互相打架的问题，让AI直接生成可拆分的部件，不用先整体生成再费力切割。意外收获是，整体质量反而比原来更好，零件匹配度提升16%。做游戏资产、3D打印、工业设计的，这是把「生成」变成「可用」的关键一步。
📄 原文摘要(英文)
Native 3D generators turn one image into a single mesh. TRELLIS.2 and its peers deliver high-fidelity non-watertight geometry with materials, but the output is one fused object, while downstream work such as editing, rigging and simulation operates on part-level assets. A naive idea is to run a 3D segmentation network on the fused mesh that TRELLIS.2 generates, but such pipelines are slow and bounded by the accuracy of the segmentation. We want a simple way to extend an existing native 3D generator to the part level. But we face a critical problem: the O-Voxel grid stores one sheet of surface per voxel, so a single volume cannot represent the interface where two parts touch, at any resolution. We introduce a dual-volume representation to solve this problem and put forward KaiNinja, a part-level extension of TRELLIS.2 built on a dual-volume form of its O-Voxel representation. KaiNinja keeps the generation speed and quality of TRELLIS.2 while extending it to the part level, with no mask or segmenter in the pipeline. Its training data come from sources of many kinds, including CAD models and assets authored by an LLM-driven agent; to our knowledge it is the first 3D generative model trained on agent-authored part data. Surprisingly, we also find that whole-object fidelity improves over the same backbone fine-tuned on the same dataset. Against part generation pipelines of different paradigms, it lowers whole-object Chamfer distance by 40% and raises strict part F-score by 16%.