Interactive motion between multiple characters is widely utilized in games and movies. However, the method for generating interactive motions considering the character's diverse mesh shape has yet to be studied. We propose a Spatio Cooperative Transformer (SCT) to retarget the interacting motions of two characters having arbitrary mesh connectivity. SCT predicts the residual of root position and joint rotations considering the shape difference between the source and target of interacting characters. In addition, we introduce an anchor loss function for SCT to maintain the geometric distance between the interacting characters when they are retargeted. We also propose a motion augmentation method with deformation-based adaptation to prepare a source-target paired dataset with an identical mesh connectivity for training. In experiments, our method achieved higher accuracy for semantic preservation and produced less artifacts of inter-penetration between the interacting characters for unseen characters and motions than the baselines. Moreover, we conducted a user evaluation using characters with various shapes, spanning low-to-high interaction levels to prove better semantic preservation of our method compared to previous studies.

中文翻译：

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用于双蒙皮角色交互的几何感知重定向


多个角色之间的交互式运动在游戏和电影中被广泛使用。然而，考虑到角色的不同网格形状，生成交互式运动的方法还有待研究。我们提出了一个空间合作转换器 （SCT） 来重新定位具有任意网格连接的两个角色的交互运动。SCT 预测根位置和关节旋转的残差，同时考虑交互角色的源和目标之间的形状差异。此外，我们还为 SCT 引入了一个锚点损失函数，以便在重定向时保持交互角色之间的几何距离。我们还提出了一种基于变形的自适应的运动增强方法，以准备具有相同网格连接的源-目标配对数据集进行训练。在实验中，我们的方法实现了更高的语义保留准确性，并且与基线相比，对于看不见的字符和动作，在交互字符之间产生的相互渗透伪影更少。此外，我们使用各种形状的字符进行了用户评估，跨越从低到高的交互水平，以证明与以前的研究相比，我们的方法具有更好的语义保留。