颗粒物：前馈式三维物体铰接

摘要

我们提出Particulate方法——一种前馈式解决方案，能够基于日常物体的单个静态三维网格，直接推断底层关节结构的所有属性，包括三维部件、运动学结构和运动约束。该方案的核心是部件关节变换器（Part Articulation Transformer），通过灵活可扩展的架构处理输入网格的点云数据，原生支持多关节预测并输出全部目标属性。我们使用公共数据集中的多样化关节化三维资产对网络进行端到端训练。在推理阶段，Particulate将网络的前馈预测结果映射至输入网格，数秒内即可生成完整的关节化三维模型，其速度远优于需要逐对象优化的现有方法。该方法还能精准推断AI生成三维资产的关节结构，当与现成的图像转三维生成器结合时，可实现从单张（真实或合成）图像中完整提取关节化三维物体。我们还基于高质量公共三维资产构建了全新的关节估计挑战性基准测试，并重新设计了更符合人类偏好的评估方案。定量与定性结果表明，Particulate在性能上显著超越现有前沿方法。

English

We present Particulate, a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying articulated structure, including its 3D parts, kinematic structure, and motion constraints. At its core is a transformer network, Part Articulation Transformer, which processes a point cloud of the input mesh using a flexible and scalable architecture to predict all the aforementioned attributes with native multi-joint support. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate lifts the network's feed-forward prediction to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate can also accurately infer the articulated structure of AI-generated 3D assets, enabling full-fledged extraction of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D generator. 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. Quantitative and qualitative results show that Particulate significantly outperforms state-of-the-art approaches.

颗粒物：前馈式三维物体铰接

Particulate: Feed-Forward 3D Object Articulation

摘要

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