Particulate: 用于前馈的3D物体关节化
Particulate: Feed-Forward 3D Object Articulation
AI总结：
Particulate通过前馈模型从3D网格推断关节结构，包括3D部分、运动结构和约束，利用Transformer网络实现高效推理，速度快于传统方法，且适用于AI生成的3D资产。
摘要：
我们介绍了Particulate，一种前馈模型，给定一个3D物体的网格，推断其关节，包括3D部分、运动结构和运动约束。该模型基于Part Articulation Transformer网络，预测所有关节的这些参数。我们通过多样化的关节3D资产数据集对网络进行端到端训练。在推理时，Particulate将网络输出映射回输入网格，几秒钟内生成完整的关节化3D模型，比需要每个对象优化的传统方法快得多。Particulate也适用于AI生成的3D资产，结合现成的图像到3D模型，可从单张（真实或合成）图像生成关节化3D对象。我们还引入了一个新的挑战性基准，从高质量的公共3D资产中定制，重新设计评估协议以更符合人类偏好。实证上，Particulate显著优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
