In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.

翻译：本文提出一种面向交互迁移任务的新方法。给定源物体与智能体之间的交互示例，该方法能够自动推断同类别目标物体与智能体之间的表面关联和空间关系，从而生成更精确有效的迁移结果。具体而言，我们的方法通过结合空间与表面表征来描述示例交互。利用学习的空间与表面联合对应场，我们将与表征相关的智能体点和物体点对应到目标物体空间，该对应场将物体表示为经形变与旋转的有符号距离场。在对应点的基础上，基于空间-表面交互表征约束与额外正则化项进行优化求解。在人体-椅子交互与手部-杯子交互迁移任务上的实验表明，我们的方法能处理源形状与目标形状之间更大的几何与拓扑差异，性能显著优于现有最优方法。