  Human pose estimation has witnessed a significant advance thanks to the
development of deep learning. Recent human pose estimation approaches tend to
directly predict the location heatmaps, which causes quantization errors and
inevitably deteriorates the performance within the reduced network output. Aim
at solving it, we revisit the heatmap-offset aggregation method and propose the
Offset-guided Network (OGN) with an intuitive but effective fusion strategy for
both two-stages pose estimation and Mask R-CNN. For two-stages pose estimation,
a greedy box generation strategy is also proposed to keep more necessary
candidates while performing person detection. For mask R-CNN, ratio-consistent
is adopted to improve the generalization ability of the network.
State-of-the-art results on COCO and PoseTrack dataset verify the effectiveness
of our offset-guided pose estimation and tracking.
