  In multi-person 2D pose estimation, the bottom-up methods simultaneously
predict poses for all persons, and unlike the top-down methods, do not rely on
human detection. However, the SOTA bottom-up methods' accuracy is still
inferior compared to the existing top-down methods. This is due to the
predicted human poses being regressed based on the inconsistent human bounding
box center and the lack of human-scale normalization, leading to the predicted
human poses being inaccurate and small-scale persons being missed. To push the
envelope of the bottom-up pose estimation, we firstly propose multi-scale
training to enhance the network to handle scale variation with single-scale
testing, particularly for small-scale persons. Secondly, we introduce dual
anatomical centers (i.e., head and body), where we can predict the human poses
more accurately and reliably, especially for small-scale persons. Moreover,
existing bottom-up methods use multi-scale testing to boost the accuracy of
pose estimation at the price of multiple additional forward passes, which
weakens the efficiency of bottom-up methods, the core strength compared to
top-down methods. By contrast, our multi-scale training enables the model to
predict high-quality poses in a single forward pass (i.e., single-scale
testing). Our method achieves 38.4\% improvement on bounding box precision and
39.1\% improvement on bounding box recall over the state of the art (SOTA) on
the challenging small-scale persons subset of COCO. For the human pose AP
evaluation, we achieve a new SOTA (71.0 AP) on the COCO test-dev set with the
single-scale testing. We also achieve the top performance (40.3 AP) on OCHuman
dataset in cross-dataset evaluation.
