Partner: Learning to Coordinate Parts for Promptable 3D Holistic Segmentation

As 3D assets possess inherent hierarchical structures, semantic granularities vary across subjective user intents. Although existing interactive methods refine individual part masks, extending them to holistic segmentation remains challenging due to boundary overlaps or semantic missing. To address this gap, we propose Partner, a learning-based framework for real-time, interactive holistic segmentation. In each round, Partner updates the entire partition based on user prompts for a single part. Through its Semantic Coordinator and adaptive Split-Fill Predictor, Partner achieves unified, instruction-guided global adjustments, outperforming heuristic baselines in both refinement quality and interaction efficiency while serving as a versatile plug-and-play module.