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。

TEMPO: 高效的多视角姿势估计、跟踪和预测

TEMPO: Efficient Multi-View Pose Estimation, Tracking, and Forecasting

We present a large-scale study on unsupervised spatiotemporal representation
learning from videos. With a unified perspective on four recent image-based
frameworks, we study a simple objective that can easily generalize all these
methods to space-time. Our objective encourages temporally-persistent features
in the same video, and in spite of its simplicity, it works surprisingly well
across: (i) different unsupervised frameworks, (ii) pre-training datasets,
(iii) downstream datasets, and (iv) backbone architectures. We draw a series of
intriguing observations from this study, e.g., we discover that encouraging
long-spanned persistency can be effective even if the timespan is 60 seconds.
In addition to state-of-the-art results in multiple benchmarks, we report a few
promising cases in which unsupervised pre-training can outperform its
supervised counterpart. Code is made available at
this https URL

本文通过对四个最新的基于图像的视角的统一，提出了一种可以很好地概括所有这些方法的简单目标，该目标鼓励同一视频中的时间持久特征，在不同的无监督框架、预训练数据集、下游数据集和骨干架构中效果惊人，我们从该研究中得出了一系列有趣的观察结果，例如，即使时间跨度为 60 秒，鼓励长时间持久性也可以很有效。