Existing volumetric methods for predicting 3D human pose estimation are
accurate, but computationally expensive and optimized for single time-step
prediction. We present TEMPO, an efficient multi-view pose estimation model
that learns a robust spatiotemporal representation, improving pose accuracy
while also tracking and forecasting human pose. We significantly reduce
computation compared to the state-of-the-art by recurrently computing
per-person 2D pose features, fusing both spatial and temporal information into
a single representation. In doing so, our model is able to use spatiotemporal
context to predict more accurate human poses without sacrificing efficiency. We
further use this representation to track human poses over time as well as
predict future poses. Finally, we demonstrate that our model is able to
generalize across datasets without scene-specific fine-tuning. TEMPO achieves
10$\%$ better MPJPE with a 33$\times$ improvement in FPS compared to TesseTrack
on the challenging CMU Panoptic Studio dataset.

TEMPO 是一种高效的多视角姿势估计模型，通过学习稳健的时空表示，提高了姿势准确性，同时还能跟踪和预测人体姿势。该模型通过循环计算个人的二维姿势特征，将空间和时间信息融合成一种表示，并利用时空上下文预测更准确的人体姿势，同时保持高效性。该模型能够跨数据集泛化而无需场景特定微调，达到了相比 TesseTrack 在具有挑战性的 CMU Panoptic Studio 数据集上 MPJPE 提升 10%，帧率提升 33 倍的效果。