SegviGen: Repurposing 3D Generative Model for Part Segmentation
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/.
69ba0d525d2b9c28ea09f07a
Lin Li
69ba0d525d2b9c28ea09f07b
Haoran Feng
claimed_verified
2026-03-18T08:44:37.883Z
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Haoran Feng
fenghora
user
fenghora
69ba0d525d2b9c28ea09f07c
Zehuan Huang
claimed_verified
2026-03-18T08:44:35.297Z
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zehuan-huang
huanngzh
user
huanngzh
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Haohua Chen
69ba0d525d2b9c28ea09f07e
Wenbo Nie
69ba0d525d2b9c28ea09f07f
Shaohua Hou
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Keqing Fan
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Pan Hu
69ba0d525d2b9c28ea09f082
Sheng Wang
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Buyu Li
69ba0d525d2b9c28ea09f084
Lu Sheng
claimed_verified
2026-03-18T08:44:33.006Z
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Lu Sheng
lsheng2024
user
lsheng2024
SegviGen: Repurposing 3D Generative Model for Part Segmentation
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/.
SegviGen repurposes pretrained 3D generative models for efficient 3D part segmentation using distinctive part colorization, achieving superior performance with minimal labeled data.
3D generative models 3D part segmentation pretrained 3D generative priors part-indicative colors voxel-based reconstruction geometry-aligned reconstruction interactive part segmentation full segmentation 2D guidance