Abstract
Part segmentation is a fundamental problem in computer graphics and 3D vision. Recent works have expanded 3D part segmentation beyond fixed taxonomies, but existing approaches typically only address a specific setting, such as text-guided part segmentation or point-based interaction. In this work, we argue that these settings can be unified as an intent-conditioned generative problem, where different prompts specify the desired part decomposition. To this end, we introduce PartLLM, a unified multimodal model that formulates 3D part segmentation as autoregressive semantic decomposition. Conditioned on an input shape and a user prompt, PartLLM autoregressively generates semantic part hypotheses as queries for mask prediction and feeds them to a decomposition-aware decoder that jointly predicts coherent part masks. This unified design supports text-guided part segmentation, interactive segmentation, and full-shape semantic decomposition with controllable granularity within a single model. Extensive experiments across these task settings show that PartLLM consistently outperforms task-specific baselines, demonstrating the effectiveness of unifying 3D part segmentation under an intent-conditioned generative formulation.
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Publication details
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- Green open access
Cite this article
APA 7
Zhu, Z., Zhang, Y., Li, P., Zhao, Z., Chen, H., Zhang, Y., Wan, L., Dou, Z., Lin, C., Liu, Y., Wei, M., & Wang, W. (2026). PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation. https://omanscience.com/en/articles/partllm-a-unified-multimodal-foundation-for-3d-part-segmentation
MLA 9
Zhu, Zhe, et al. "PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation." https://omanscience.com/en/articles/partllm-a-unified-multimodal-foundation-for-3d-part-segmentation.
Chicago (author–date)
Zhu, Zhe, Yiheng Zhang, Peng Li, Zixing Zhao, Honghua Chen, Yaqing Zhang, Le Wan, Zhiyang Dou, Cheng Lin, Yuan Liu, Mingqiang Wei, and Wenping Wang. 2026. "PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation." https://omanscience.com/en/articles/partllm-a-unified-multimodal-foundation-for-3d-part-segmentation.
Harvard
Zhu, Z., Zhang, Y., Li, P., Zhao, Z., Chen, H., Zhang, Y., Wan, L., Dou, Z., Lin, C., Liu, Y., Wei, M. and Wang, W. (2026) 'PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation', Available at: https://omanscience.com/en/articles/partllm-a-unified-multimodal-foundation-for-3d-part-segmentation.
Vancouver
Zhu Z, Zhang Y, Li P, Zhao Z, Chen H, Zhang Y, et al. PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation. https://omanscience.com/en/articles/partllm-a-unified-multimodal-foundation-for-3d-part-segmentation
IEEE
Z. Zhu, Y. Zhang, P. Li, Z. Zhao, H. Chen, Y. Zhang, L. Wan, Z. Dou, C. Lin, Y. Liu, M. Wei, and W. Wang, "PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation," https://omanscience.com/en/articles/partllm-a-unified-multimodal-foundation-for-3d-part-segmentation.