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.

通过引入一种新的基于解剖学的语义矩阵（ASM），本文旨在在不同的角色模型之间传输手势的语义以实现精确的手势转换。使用基于解剖学的语义重建网络（ASRN）来获取源 ASM 与目标手关节旋转之间的映射函数，并通过半监督学习策略在 Mixamo 和 InterHand2.6M 数据集上训练 ASRN，定性和定量结果证明了 ASRN 相比现有技术的显著优势。