We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or multi-view mask aggregation, often suffering from cross-view inconsistency and blurred boundaries, or explore native 3D discriminative segmentation, which typically requires large-scale annotated 3D data and substantial training resources. In contrast, SegviGen leverages the structured priors encoded in pretrained 3D generative model to induce segmentation through distinctive part colorization, establishing a novel and efficient framework for part segmentation. Specifically, SegviGen encodes a 3D asset and predicts part-indicative colors on active voxels of a geometry-aligned reconstruction. It supports interactive part segmentation, full segmentation, and full segmentation with 2D guidance in a unified framework. Extensive experiments show that SegviGen improves over the prior state of the art by 40% on interactive part segmentation and by 15% on full segmentation, while using only 0.32% of the labeled training data. It demonstrates that pretrained 3D generative priors transfer effectively to 3D part segmentation, enabling strong performance with limited supervision. See our project page at https://fenghora.github.io/SegviGen-Page/.

翻译：我们提出了SegviGen框架，该框架将原生三维生成模型重新用于三维部件分割。现有方法通常通过蒸馏或多视角掩码聚合将强二维先验提升至三维，常面临跨视角不一致性和边界模糊问题；或探索原生三维判别式分割，但通常需要大规模标注三维数据和大量训练资源。相比之下，SegviGen利用预训练三维生成模型中编码的结构化先验，通过差异化部件着色实现分割，建立了一种新颖高效的部件分割框架。具体而言，SegviGen对三维资产进行编码，并在几何对齐重建的活跃体素上预测部件指示性颜色。该框架在统一架构中支持交互式部件分割、完整分割以及二维引导的完整分割。大量实验表明，SegviGen在交互式部件分割任务上比现有最优方法提升40%，在完整分割任务上提升15%，且仅使用0.32%的标注训练数据。这证明预训练三维生成先验能有效迁移至三维部件分割任务，在有限监督条件下实现卓越性能。项目页面详见：https://fenghora.github.io/SegviGen-Page/。