  Multi-person pose estimation from a 2D image is challenging because it
requires not only keypoint localization but also human detection. In
state-of-the-art top-down methods, multi-scale information is a crucial factor
for the accurate pose estimation because it contains both of local information
around the keypoints and global information of the entire person. Although
multi-scale information allows these methods to achieve the state-of-the-art
performance, the top-down methods still require a huge amount of computation
because they need to use an additional human detector to feed the cropped human
image to their pose estimation model. To effectively utilize multi-scale
information with the smaller computation, we propose a multi-scale aggregation
R-CNN (MSA R-CNN). It consists of multi-scale RoIAlign block (MS-RoIAlign) and
multi-scale keypoint head network (MS-KpsNet) which are designed to effectively
utilize multi-scale information. Also, in contrast to previous top-down
methods, the MSA R-CNN performs human detection and keypoint localization in a
single model, which results in reduced computation. The proposed model achieved
the best performance among single model-based methods and its results are
comparable to those of separated model-based methods with a smaller amount of
computation on the publicly available 2D multi-person keypoint localization
dataset.
