Abstract
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.
Abstract (translated)
在本文中，我们提出了一种新的交互转移方法。给定一个源对象和一个代理之间的交互示例，我们的方法可以自动推断同一类别中代理和目标对象之间的表面和空间关系，从而产生更准确和有效的转移。具体来说，我们的方法通过结合空间和表面表示来描述示例交互。我们将代理点和对表示的物体点与目标对象空间中的点相对应，使用学习到的空间和表面匹配场将物体表示为变形和旋转的签名距离场。在满足我们的空间和表面交互表示的约束条件下，进行优化。在人类椅和手拿咖啡杯的交互转移任务上进行实验证明，我们的方法可以处理源和目标形状之间更大的几何和拓扑变化，显著优于现有方法。
URL
https://arxiv.org/abs/2405.03221
