Part-X-MLLM: Part-aware 3D Multimodal Large Language Model
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914161482530816 |
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| author | Wang, Chunshi Ye, Junliang Yang, Yunhan Li, Yang Lin, Zizhuo Zhu, Jun Chen, Zhuo Luo, Yawei Guo, Chunchao |
| author_facet | Wang, Chunshi Ye, Junliang Yang, Yunhan Li, Yang Lin, Zizhuo Zhu, Jun Chen, Zhuo Luo, Yawei Guo, Chunchao |
| contents | We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level bounding boxes, semantic descriptions, and edit commands. This structured output serves as a versatile interface to drive downstream geometry-aware modules for part-based generation and editing. By decoupling the symbolic planning from the geometric synthesis, our approach allows any compatible geometry engine to be controlled through a single, language-native frontend. We pre-train a dual-encoder architecture to disentangle structure from semantics and instruction-tune the model on a large-scale, part-centric dataset. Experiments demonstrate that our model excels at producing high-quality, structured plans, enabling state-of-the-art performance in grounded Q\&A, compositional generation, and localized editing through one unified interface. Project page: https://chunshi.wang/Part-X-MLLM/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Part-X-MLLM: Part-aware 3D Multimodal Large Language Model Wang, Chunshi Ye, Junliang Yang, Yunhan Li, Yang Lin, Zizhuo Zhu, Jun Chen, Zhuo Luo, Yawei Guo, Chunchao Computer Vision and Pattern Recognition We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level bounding boxes, semantic descriptions, and edit commands. This structured output serves as a versatile interface to drive downstream geometry-aware modules for part-based generation and editing. By decoupling the symbolic planning from the geometric synthesis, our approach allows any compatible geometry engine to be controlled through a single, language-native frontend. We pre-train a dual-encoder architecture to disentangle structure from semantics and instruction-tune the model on a large-scale, part-centric dataset. Experiments demonstrate that our model excels at producing high-quality, structured plans, enabling state-of-the-art performance in grounded Q\&A, compositional generation, and localized editing through one unified interface. Project page: https://chunshi.wang/Part-X-MLLM/ |
| title | Part-X-MLLM: Part-aware 3D Multimodal Large Language Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.13647 |