PosePropagationNet: Towards Accurate and Efficient Pose Estimation in Videos

Abstract

We rethink on the contradiction between accuracy and efficiency in the field of video pose estimation. Large networks are typically exploited in previous methods to pursue superior pose estimation results. However, those methods can hardly meet the low-latency requirement for real-time applications because of their computationally expensive nature. We present a novel architecture, PosePropagation-Net (PPN), to generate poses across video frames accurately and efficiently. Instead of extracting temporal cues or knowledge someways to enforce geometric consistency as most of the previous methods do, we explicitly propagate well-estimated pose from the preceding frame to the current frame by leveraging pose propagation mechanism, endowing lightweight networks with the capability of performing accurate pose estimation in videos. The experiments on two large-scale benchmarks for video pose estimation show that our method significantly outperforms previous state-of-the-art methods in both accuracy and efficiency. Compared with the previous best method, our two representative configurations, PPN-Stable and PPN-Swift, achieve 2.5× and 6× FLOPs reduction respectively, as well as significant accuracy improvement. INDEX TERMS Network efficiency, pose propagation mechanism, video pose estimation.

Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

“…Table 2 shows the state-of-the-art performance of Pose for Action [51], Thin-Slicing Networks [52], LSTM Pose Machines [53], DKD Efficient Pose [54], PosePropagationNet [3], and our proposed work on Penn Action benchmark datasets. Among these models the Pose for Action has lowest evaluation results only 81.1% for PCK-body and 92.6 % for PCK-torso, which is designed by VGG-16 backbone architecture.…”

“…The pose estimation network modifies joint motion offsets by ensuring feature consistency across the frames from previous to next frame. The single person pose estimation model creates heatmaps with basic information of each articulation for body joint coordinate [3]. Which is generated by utilizing heatmap choice scales with minimal overhead.…”