一键解析3D物体关节结构，秒级生成可动模型。
Particulate: Feed-Forward 3D Object Articulation
- 用Transformer直接预测物体的部件与运动约束。
- 在真实与生成3D数据上均显著优于现有方法。
- 适合需要快速生成可动3D模型的研究与开发人员。
我们提出Particulate，一种前馈式模型，给定一个3D网格，即可推断其关节结构，包括3D部件、运动关系和约束。该模型基于部分关节Transformer（Part Articulation Transformer），一次性预测所有关节的参数。在来自公开数据集的多样化3D资产上端到端训练。推理时，模型将输出映射回输入网格，秒级生成完整可动3D模型，远快于需逐对象优化的旧方法。该模型也适用于AI生成的3D资产，结合现成的图像到3D模型，仅需单张图像即可生成可动3D物体。我们还构建了一个新基准，从高质量公共3D资产中精选，重新设计评估协议以更符合人类偏好。实验证明，Particulate显著超越当前最优方法。
原文摘要 · Abstract (English)
We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate maps the output of the network back to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate also works on AI-generated 3D assets, enabling the generation of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D model. We further introduce a new challenging benchmark for 3D articulation estimation curated from high-quality public 3D assets, and redesign the evaluation protocol to be more consistent with human preferences. Empirically, Particulate significantly outperforms state-of-the-art approaches.
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