In this paper, we are interested in bottom-up multiperson human pose estimation. A typical bottom-up pipeline consists of two main steps: heatmap prediction and keypoint grouping. We mainly focus on the first step for improving heatmap prediction accuracy. We propose Higher-Resolution Network (HigherHRNet), which is a simple extension of the High-Resolution Network (HRNet). High-erHRNet generates higher-resolution feature maps by deconvolving the high-resolution feature maps outputted by HRNet, which are spatially more accurate for small and medium persons. Then, we build high-quality multi-level features and perform multi-scale pose prediction. The extra computation overhead is marginal and negligible in comparison to existing bottom-up methods that rely on multiscale image pyramids or large input image size to generate accurate pose heatmaps. HigherHRNet surpasses all existing bottom-up methods on the COCO dataset without using multi-scale test. The code and models will be released. * This work was done while Bin Xiao was at Microsoft. CNN (a) Upsampling input image. Heatmap Aggregation CNN CNN CNN (b) Multi-scale test. Heatmap Aggregation HRNet (c) Our approach.
HigherHR-Net REF maintains high-resolution feature maps which effectively improves the precision of the predictions.
Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation.
ArXiv
ArXiv
Computer Science