When analyzing human motion videos, the output jitters from existing pose estimators are highly-unbalanced. Most frames only suffer from slight jitters, while significant jitters occur in those frames with occlusion or poor image quality. Such complex poses often persist in videos, leading to consecutive frames with poor estimation results and large jitters. Existing pose smoothing solutions based on temporal convolutional networks, recurrent neural networks, or low-pass filters cannot deal with such a long-term jitter problem without considering the significant and persistent errors within the jittering video segment. Motivated by the above observation, we propose a novel plug-and-play refinement network, namely SMOOTHNET, which can be attached to any existing pose estimators to improve its temporal smoothness and enhance its per-frame precision simultaneously. Especially, SMOOTHNET is a simple yet effective data-driven fully-connected network with large receptive fields, effectively mitigating the impact of long-term jitters with unreliable estimation results. We conduct extensive experiments on twelve backbone networks with seven datasets across 2D and 3D pose estimation, body recovery, and downstream tasks. Our results demonstrate that the proposed SMOOTHNET consistently outperforms existing solutions, especially on those clips with high errors and long-term jitters.

本文介绍了一种叫做SmoothNet的基于时间的姿势估计模型，通过学习每个关节的长期时间关系来改善现有姿势估计器的时间平滑度，从而提高估计准确性，并展示了在五个数据集上的实验结果。

SmoothNet: 用于视频中优化人体姿态的即插即用网络