论文标题
3D运动重建的新时空损失函数和运动评估的扩展时间指标
A New Spatio-Temporal Loss Function for 3D Motion Reconstruction and Extended Temporal Metrics for Motion Evaluation
论文作者
论文摘要
我们提出了一个新的损失函数，我们将其称为Laplacian损失，基于该运动作为图形的时空拉普拉斯表示。该损失函数旨在通过视频中的3D人姿势估算进行运动重建训练模型。它比较了从地面真理的图表示与估计值之一获得的关节的差分坐标。我们设计了一个完全卷积的时间网络，以进行运动重建，以实现估计的更好的时间一致性。我们使用这种通用模型来研究我们提出的损失函数对人类提供的基准为360万的基准的影响。我们还利用各种运动描述符，例如速度，加速度来对时间一致性进行彻底评估，同时将结果与某些最新的解决方案进行比较。
We propose a new loss function that we call Laplacian loss, based on spatio-temporal Laplacian representation of the motion as a graph. This loss function is intended to be used in training models for motion reconstruction through 3D human pose estimation from videos. It compares the differential coordinates of the joints obtained from the graph representation of the ground truth against the one of the estimation. We design a fully convolutional temporal network for motion reconstruction to achieve better temporal consistency of estimation. We use this generic model to study the impact of our proposed loss function on the benchmarks provided by Human3.6M. We also make use of various motion descriptors such as velocity, acceleration to make a thorough evaluation of the temporal consistency while comparing the results to some of the state-of-the-art solutions.
