颗粒物：前馈式三维物体铰接

摘要

我们提出Particulate——一种前馈方法，能够基于日常物体的单个静态三维网格，直接推断底层铰接结构的所有属性，包括三维部件、运动学结构和运动约束。其核心是部件铰接变换器（Part Articulation Transformer），该变换器网络通过灵活可扩展的架构处理输入网格的点云数据，原生支持多关节属性预测。我们使用公开数据集中多样化的铰接三维资源对网络进行端到端训练。在推理过程中，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铰接的三维模型，其速度远超需要逐对象优化的现有方法。当与现成的图像转三维生成器结合时，Particulate还能准确推断AI生成三维资源的铰接结构，实现从单张（真实或合成）图像中完整提取铰接三维物体。我们还基于高质量公开三维资源构建了新的铰接估计挑战性基准，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显著优于现有最先进方法。

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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