Hand motion capture data is now relatively easy to obtain, even for complicated grasps; however this data is of limited use without the ability to retarget it onto the hands of a specific character or robot. The target hand may differ dramatically in geometry, number of degrees of freedom (DOFs), or number of fingers. We present a simple, but effective framework capable of kinematically retargeting multiple human hand-object manipulations from a publicly available dataset to a wide assortment of kinematically and morphologically diverse target hands through the exploitation of contact areas. We do so by formulating the retarget operation as a non-isometric shape matching problem and use a combination of both surface contact and marker data to progressively estimate, refine, and fit the final target hand trajectory using inverse kinematics (IK). Foundational to our framework is the introduction of a novel shape matching process, which we show enables predictable and robust transfer of contact data over full manipulations while providing an intuitive means for artists to specify correspondences with relatively few inputs. We validate our framework through thirty demonstrations across five different hand shapes and six motions of different objects. We additionally compare our method against existing hand retargeting approaches. Finally, we demonstrate our method enabling novel capabilities such as object substitution and the ability to visualize the impact of design choices over full trajectories.

翻译：手部动作捕捉数据如今已相对容易获取，即便针对复杂抓取场景亦是如此；然而，若无法将这些数据重定向至特定角色或机器人的手部，其应用价值将十分有限。目标手部在几何形态、自由度数量或手指数量上可能存在显著差异。我们提出一个简洁但有效的框架，通过利用接触区域，能够将来自公开数据集的多个人类手部-物体操作动作，以运动学方式重定向至形态与运动学结构各异的多种目标手部。具体而言，我们将重定向操作建模为一个非等距形状匹配问题，并结合表面接触数据与标记点数据，通过逆运动学逐步估计、优化并拟合最终的目标手部轨迹。该框架的核心在于引入一种新颖的形状匹配过程，我们证明其能够实现整个操作过程中接触数据的可预测鲁棒迁移，同时为艺术家提供了一种直观方式，使其可通过较少的输入指定对应关系。我们通过涵盖五种不同手部形状及六种不同物体运动的三十组演示实验验证了该框架的有效性，并将其与现有手部重定向方法进行了对比。此外，我们还展示了该方法在物体替换、以及可视化设计选择对完整轨迹影响等新功能中的应用潜力。