  A key assumption of top-down human pose estimation approaches is their
expectation of having a single person present in the input bounding box. This
often leads to failures in crowded scenes with occlusions. We propose a novel
solution to overcome the limitations of this fundamental assumption. Our
Multi-Hypothesis Pose Network (MHPNet) allows for predicting multiple 2D poses
within a given bounding box. We introduce a Multi-Hypothesis Attention Block
(MHAB) that can adaptively modulate channel-wise feature responses for each
hypothesis and is parameter efficient. We demonstrate the efficacy of our
approach by evaluating on COCO, CrowdPose, and OCHuman datasets. Specifically,
we achieve 70.0 AP on CrowdPose and 42.5 AP on OCHuman test sets, a significant
improvement of 2.4 AP and 6.5 AP over the prior art, respectively. When using
ground truth bounding boxes for inference, MHPNet achieves an improvement of
0.7 AP on COCO, 0.9 AP on CrowdPose, and 9.1 AP on OCHuman validation sets
compared to HRNet. Interestingly, when fewer, high confidence bounding boxes
are used, HRNet's performance degrades (by 5 AP) on OCHuman, whereas MHPNet
maintains a relatively stable performance (a drop of 1 AP) for the same inputs.
