ArtLLM: 通过3D语言模型生成拟合资产
ArtLLM: Generating Articulated Assets via 3D LLM
AI总结：
ArtLLM通过3D语言模型直接从完整3D网格生成高质量拟合资产，解决了传统方法在关节拟合和部分检索中的局限，提升了部分布局和关节预测的准确性。
摘要：
创建交互式数字环境依赖于具有功能性的拟合3D对象，但现有方法受限于优化和检索。ArtLLM引入了基于大规模拟合数据集的3D多模态大语言模型，自回归预测可变数量的部分和关节，统一推断运动结构，从而生成高保真的部分几何。实验表明ArtLLM在PartNet-Mobility数据集上优于现有方法，且能稳健泛化到真实物体，并用于构建数字孪生，展示了其在机器人学习中的潜力。
英文摘要：
Creating interactive digital environments for gaming, robotics, and simulation relies on articulated 3D objects whose functionality emerges from their part geometry and kinematic structure. However, existing approaches remain fundamentally limited: optimization-based reconstruction methods require slow, per-object joint fitting and typically handle only simple, single-joint objects, while retrieval-based methods assemble parts from a fixed library, leading to repetitive geometry and poor generalization. To address these challenges, we introduce ArtLLM, a novel framework for generating high-quality articulated assets directly from complete 3D meshes. At its core is a 3D multimodal large language model trained on a large-scale articulation dataset curated from both existing articulation datasets and procedurally generated objects. Unlike prior work, ArtLLM autoregressively predicts a variable number of parts and joints, inferring their kinematic structure in a unified manner from the object's point cloud. This articulation-aware layout then conditions a 3D generative model to synthesize high-fidelity part geometries. Experiments on the PartNet-Mobility dataset show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction, while generalizing robustly to real-world objects. Finally, we demonstrate its utility in constructing digital twins, highlighting its potential for scalable robot learning.
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