Dimensions overall score 8.0

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• SegviGen improves over the prior state of the art by 15% on full segmentation.result 90%
• SegviGen uses only 0.32% of the labeled training data compared to existing methods.technical 90%
• SegviGen improves over the prior state of the art by 40% on interactive part segmentation.result 90%
• SegviGen leverages structured priors encoded in pretrained 3D generative models to induce segmentation through distinctive part colorization.method 85%
• SegviGen supports interactive part segmentation, full segmentation, and full segmentation with 2D guidance in a unified framework.technical 85%
• Existing 3D part segmentation pipelines suffer from cross-view inconsistency and blurred boundaries when lifting 2D priors into 3D.limitation 80%
• Native 3D discriminative segmentation typically requires large-scale annotated 3D data and substantial training resources.limitation 80%
• Pretrained 3D generative priors transfer effectively to 3D part segmentation, enabling strong performance with limited supervision.result 85%

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