Estimating human pose from video is a task that receives considerable attention due to its applicability in numerous 3D fields. The complexity of prior knowledge of human body movements poses a challenge to neural network models in the task of regressing keypoints. In this paper, we address this problem by incorporating motion prior in an adversarial way. Different from previous methods, we propose to decompose holistic motion prior to joint motion prior, making it easier for neural networks to learn from prior knowledge thereby boosting the performance on the task. We also utilize a novel regularization loss to balance accuracy and smoothness introduced by motion prior. Our method achieves 9\% lower PA-MPJPE and 29\% lower acceleration error than previous methods tested on 3DPW. The estimator proves its robustness by achieving impressive performance on in-the-wild dataset.

中文翻译：

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通过对抗训练进行视频姿态估计的分解人体运动先验

从视频中估计人体姿态是一项受到广泛关注的任务，因为它适用于众多 3D 领域。人体运动先验知识的复杂性对神经网络模型在回归关键点的任务中提出了挑战。在本文中，我们通过以对抗方式结合先验运动来解决这个问题。与以前的方法不同，我们建议在关节运动之前先分解整体运动，使神经网络更容易从先验知识中学习，从而提高任务的性能。我们还利用一种新颖的正则化损失来平衡运动先验引入的准确性和平滑度。与之前在 3DPW 上测试的方法相比，我们的方法实现了 9% 的 PA-MPJPE 降低和 29% 的加速误差降低。