Multi-frame human pose estimation in complicated situations is challenging.
Although state-of-the-art human joints detectors have demonstrated remarkable
results for static images, their performances come short when we apply these
models to video sequences. Prevalent shortcomings include the failure to handle
motion blur, video defocus, or pose occlusions, arising from the inability in
capturing the temporal dependency among video frames. On the other hand,
directly employing conventional recurrent neural networks incurs empirical
difficulties in modeling spatial contexts, especially for dealing with pose
occlusions. In this paper, we propose a novel multi-frame human pose estimation
framework, leveraging abundant temporal cues between video frames to facilitate
keypoint detection. Three modular components are designed in our framework. A
Pose Temporal Merger encodes keypoint spatiotemporal context to generate
effective searching scopes while a Pose Residual Fusion module computes
weighted pose residuals in dual directions. These are then processed via our
Pose Correction Network for efficient refining of pose estimations. Our method
ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the
large-scale benchmark datasets PoseTrack2017 and PoseTrack2018. We have
released our code, hoping to inspire future research.

本文提出了一种基于多帧和时序信息的人体姿态估计方法。该方法包含三个模块：姿态时序合成器、姿态残差融合模块和姿态校正网络。在 PoseTrack2017 和 PoseTrack2018 数据集上进行的实验结果表明，该方法取得了最佳效果，并已发布代码以期促进未来的研究。