Human hands, the primary means of non-verbal communication, convey intricate semantics in various scenarios. Due to the high sensitivity of individuals to hand motions, even minor errors in hand motions can significantly impact the user experience. Real applications often involve multiple avatars with varying hand shapes, highlighting the importance of maintaining the intricate semantics of hand motions across the avatars. Therefore, this paper aims to transfer the hand motion semantics between diverse avatars based on their respective hand models. To address this problem, we introduce a novel anatomy-based semantic matrix (ASM) that encodes the semantics of hand motions. The ASM quantifies the positions of the palm and other joints relative to the local frame of the corresponding joint, enabling precise retargeting of hand motions. Subsequently, we obtain a mapping function from the source ASM to the target hand joint rotations by employing an anatomy-based semantics reconstruction network (ASRN). We train the ASRN using a semi-supervised learning strategy on the Mixamo and InterHand2.6M datasets. We evaluate our method in intra-domain and cross-domain hand motion retargeting tasks. The qualitative and quantitative results demonstrate the significant superiority of our ASRN over the state-of-the-arts.

翻译：人类双手作为非语言交流的主要媒介，在多种场景中传递着复杂的语义信息。由于个体对手部动作的高度敏感性，即使是微小的手部动作误差也会显著影响用户体验。实际应用中常涉及多种手型的虚拟角色，因此保持手部动作在不同角色间的精细语义至关重要。本文旨在基于不同角色的手部模型实现手部动作语义的跨角色迁移。针对该问题，我们提出了一种新颖的解剖学语义矩阵（ASM），该矩阵对手部动作语义进行编码。ASM通过量化手掌及其他关节相对于对应关节局部坐标系的位置，实现了手部动作的精确重定向。随后，我们采用基于解剖学的语义重建网络（ASRN），从源ASM中推导出目标手部关节旋转的映射函数。基于Mixamo和InterHand2.6M数据集，我们采用半监督学习策略训练ASRN。在域内与跨域手部动作重定向任务中验证了本方法的有效性。定性与定量结果表明，我们的ASRN显著优于当前最先进方法。