BimArt：一种用于合成与关节物体进行3D双手交互的统一方法
BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects
AI总结：
BimArt提出一种无需参考抓取或轨迹的统一生成方法，通过关节感知接触图引导手部运动生成，实现多样逼真的3D双手与关节物体交互，在质量和多样性上超越现有技术。
摘要：
我们提出了BimArt，一种用于合成与关节物体进行3D双手交互的新型生成方法。与先前的工作不同，我们不依赖于参考抓取、粗略的手部轨迹或用于抓取和关节运动的独立模式。为实现这一目标，我们首先根据物体轨迹生成基于距离的接触图，并采用具有关节感知能力的特征表示，从而揭示丰富的双手操作模式。随后，学习到的接触先验被用于引导我们的手部运动生成器，为物体移动和关节运动生成多样且逼真的双手运动。我们的工作为关节物体的特征表示和接触先验提供了关键见解，证明了它们在控制复杂高维的双手-物体交互空间方面的有效性。通过全面的定量实验，我们展示了在运动质量和多样性方面超越现有技术水平，向简化且高质量的手部-物体动画迈出的清晰一步。项目页面：https://vcai.mpi-inf.mpg.de/projects/bimart/。
英文摘要：
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 surpass the state of the art in motion quality and diversity. Project page: https://vcai.mpi-inf.mpg.de/projects/bimart/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
