Establishing a human motion reconstruction system from very few constraints imposed on the body has been an interesting and important research topic because it significantly reduces the degrees of freedom to be managed. However, it is a well-known mathematically ill-posed problem as the dimension of constraints is much lower than that of the human pose to be determined. Therefore, it is challenging to directly reconstruct the whole body joint information from very few constraints due to many possible solutions. To address this issue, we present a novel deep learning framework with an attention mechanism using large-scale motion capture (mocap) data for mapping very few user-defined constraints into the human motion as realistically as possible. Our system is built upon the attention networks for looking back further to achieve better results. Experimental results show that our network model is capable of producing more accurate results compared with previous approaches. We also conducted several experiments to test all possible combinations of the features extracted from the mocap data, and found the best feature combination to generate high-quality poses.

从对身体施加的极少约束中建立人体运动重建系统一直是一个有趣且重要的研究课题，因为它显着降低了要管理的自由度。然而，这是一个众所周知的数学不适定问题，因为约束的维度远低于要确定的人体姿势的维度。因此，由于许多可能的解决方案，从很少的约束中直接重建全身关节信息是具有挑战性的。为了解决这个问题，我们提出了一种新颖的深度学习框架，该框架具有使用大规模运动捕捉 (mocap) 数据的注意力机制，可将极少的用户定义约束尽可能真实地映射到人体运动中。我们的系统建立在注意力网络上，以便进一步回顾以获得更好的结果。实验结果表明，与以前的方法相比，我们的网络模型能够产生更准确的结果。我们还进行了多次实验来测试从 mocap 数据中提取的特征的所有可能组合，并找到生成高质量姿势的最佳特征组合。