We address the challenge of generating 3D articulated objects in a controllable fashion. Currently, modeling articulated 3D objects is either achieved through laborious manual authoring, or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape, connectivity, and motion using a denoising diffusion-based method with attention modules designed to extract correlations between part attributes. Our method takes an object category label and a part connectivity graph as input and generates an object's geometry and motion parameters. The generated objects conform to user-specified constraints on the object category, part shape, and part articulation. Our experiments show that our method outperforms the state-of-the-art in articulated object generation, producing more realistic objects while conforming better to user constraints. Video Summary at: http://youtu.be/cH_rbKbyTpE

我们通过使用去噪扩散方法和注意力模块来提取零件属性之间的相关性，以期缩放和直接控制地生成符合用户约束的物体的几何形状和运动参数，其中3D关节物体的建模通常通过繁琐的手动创作或难以扩展和直接控制的先前方法实现。实验结果表明，我们的方法在生成关节物体方面优于现有技术，在产生更逼真的物体同时更好地符合用户约束。

CAGE：可控关节生成