Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods directly employ optical flow or deformable convolution to predict full-spectrum motion fields, which might incur numerous irrelevant cues, such as a nearby person or background. Without further efforts to excavate meaningful motion priors, their results are suboptimal, especially in complicated spatiotemporal interactions. On the other hand, the temporal difference has the ability to encode representative motion information which can potentially be valuable for pose estimation but has not been fully exploited. In this paper, we present a novel multi-frame human pose estimation framework, which employs temporal differences across frames to model dynamic contexts and engages mutual information objectively to facilitate useful motion information disentanglement. To be specific, we design a multi-stage Temporal Difference Encoder that performs incremental cascaded learning conditioned on multi-stage feature difference sequences to derive informative motion representation. We further propose a Representation Disentanglement module from the mutual information perspective, which can grasp discriminative task-relevant motion signals by explicitly defining useful and noisy constituents of the raw motion features and minimizing their mutual information. These place us to rank No.1 in the Crowd Pose Estimation in Complex Events Challenge on benchmark dataset HiEve, and achieve state-of-the-art performance on three benchmarks PoseTrack2017, PoseTrack2018, and PoseTrack21.

翻译：时序建模对于多帧人体姿态估计至关重要。现有方法大多直接采用光流或可变形卷积来预测全频谱运动场，这可能会引入大量无关线索（如邻近人物或背景）。由于缺乏进一步挖掘有意义的运动先验，这些方法在复杂的时空交互场景中效果欠佳。另一方面，时间差分能够编码具有代表性的运动信息，对姿态估计具有潜在价值，但尚未被充分利用。本文提出一种新颖的多帧人体姿态估计框架，该框架利用跨帧时间差分建模动态上下文，并客观引入互信息以促进有用运动信息的解耦。具体而言，我们设计了一个多阶段时间差分编码器，该编码器基于多阶段特征差分序列进行增量级联学习，从而推导出富含信息的运动表征。我们还从互信息角度提出了表征解耦模块，通过明确定义原始运动特征中有用和噪声成分并最小化它们的互信息，来捕获与任务相关的判别性运动信号。这些方法使我们在基准数据集HiEve的复杂事件人群姿态估计挑战中排名第一，并在PoseTrack2017、PoseTrack2018和PoseTrack21三个基准测试上达到最优性能。