论文标题
3D人姿势估计具有自适应接受场和扩张时间卷积的估计
3D human pose estimation with adaptive receptive fields and dilated temporal convolutions
论文作者
论文摘要
在这项工作中，我们证明3D姿势估计中的接受场可以使用光流有效地指定。我们介绍了自适应接受场，这是一种基于光流推断的姿势估计模型中有助于接受场选择的简单有效方法。我们将在固定接收场上运行的基准最先进模型与其自适应场对比。通过使用降低的接收场，我们的模型可以处理慢动作序列（长10倍）比以常规速度运行的基准模型快23％。在产生姿势预测准确性至基准模型的0.36％以内时，计算成本的降低是实现的。
In this work, we demonstrate that receptive fields in 3D pose estimation can be effectively specified using optical flow. We introduce adaptive receptive fields, a simple and effective method to aid receptive field selection in pose estimation models based on optical flow inference. We contrast the performance of a benchmark state-of-the-art model running on fixed receptive fields with their adaptive field counterparts. By using a reduced receptive field, our model can process slow-motion sequences (10x longer) 23% faster than the benchmark model running at regular speed. The reduction in computational cost is achieved while producing a pose prediction accuracy to within 0.36% of the benchmark model.
