Title: GEOPARD: Geometric Pretraining for Articulation Prediction in 3D Shapes Show Chinese title

Title: GEOPARD：用于三维形状运动预测的几何预训练

Abstract: We present GEOPARD, a transformer-based architecture for predicting articulation from a single static snapshot of a 3D shape. The key idea of our method is a pretraining strategy that allows our transformer to learn plausible candidate articulations for 3D shapes based on a geometric-driven search without manual articulation annotation. The search automatically discovers physically valid part motions that do not cause detachments or collisions with other shape parts. Our experiments indicate that this geometric pretraining strategy, along with carefully designed choices in our transformer architecture, yields state-of-the-art results in articulation inference in the PartNet-Mobility dataset. Show Chinese abstract

Abstract: 我们提出了GEOPARD，这是一种基于变换器的架构，用于从3D形状的单一静态快照预测其关节活动。 我们方法的关键思想是一种预训练策略，允许我们的变换器根据几何驱动的搜索学习3D形状的合理候选关节活动，而无需人工标注关节活动。 搜索会自动发现不会导致分离或与其他形状部分发生碰撞的物理有效的部件运动。 我们的实验表明，这种几何预训练策略，加上我们在变换器架构中精心设计的选择，在PartNet-Mobility数据集的关节活动推断方面取得了最先进的结果。