论文标题
运动通过深$δ$ - 间接剂的运动
Motion Inbetweening via Deep $Δ$-Interpolator
论文作者
论文摘要
我们表明，如果使用球形线性线性插装器作为基线，可以更准确，有效地解决在一组关键帧中综合人类运动的任务。我们从经验上证明了我们在实现最先进性能的公开数据集上的方法的实力。我们通过表明$δ$ - 优势相对于最后已知帧的参考（也称为零速度模型），进一步概括了这些结果。这支持了一个更一般的结论，即在参考框架本地对输入帧的工作比以前的工作中主张的全球（世界）参考框架更准确，更健壮。我们的代码可在https://github.com/boreshkinai/delta-interpolator上公开获取。
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the $Δ$-regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion that operating in the reference frame local to input frames is more accurate and robust than in the global (world) reference frame advocated in previous work. Our code is publicly available at https://github.com/boreshkinai/delta-interpolator.
