We present BimArt, a novel generative approach for synthesizing 3D bimanual hand interactions with articulated objects. Unlike prior works, we do not rely on a reference grasp, a coarse hand trajectory, or separate modes for grasping and articulating. To achieve this, we first generate distance-based contact maps conditioned on the object trajectory with an articulation-aware feature representation, revealing rich bimanual patterns for manipulation. The learned contact prior is then used to guide our hand motion generator, producing diverse and realistic bimanual motions for object movement and articulation. Our work offers key insights into feature representation and contact prior for articulated objects, demonstrating their effectiveness in taming the complex, high-dimensional space of bimanual hand-object interactions. Through comprehensive quantitative experiments, we demonstrate a clear step towards simplified and high-quality hand-object animations that excel over the state-of-the-art in motion quality and diversity.

翻译：我们提出BimArt，一种用于合成与关节化物体三维双手交互的新型生成方法。与先前工作不同，我们不依赖参考抓取、粗略手部轨迹或分离的抓取与关节操作模式。为实现这一目标，我们首先基于物体轨迹生成距离接触图，该过程采用关节感知特征表示，从而揭示丰富的双手操作模式。随后，学习得到的接触先验被用于指导手部运动生成器，产生多样化且逼真的物体移动与关节操作双手运动。我们的工作为关节化物体的特征表示与接触先验提供了关键见解，证明了其在驯服复杂高维双手-物体交互空间中的有效性。通过全面的定量实验，我们展示了在简化且高质量的手-物体动画生成方面取得的显著进展，其在运动质量与多样性方面均优于现有最先进方法。