  We present a box-free bottom-up approach for the tasks of pose estimation and
instance segmentation of people in multi-person images using an efficient
single-shot model. The proposed PersonLab model tackles both semantic-level
reasoning and object-part associations using part-based modeling. Our model
employs a convolutional network which learns to detect individual keypoints and
predict their relative displacements, allowing us to group keypoints into
person pose instances. Further, we propose a part-induced geometric embedding
descriptor which allows us to associate semantic person pixels with their
corresponding person instance, delivering instance-level person segmentations.
Our system is based on a fully-convolutional architecture and allows for
efficient inference, with runtime essentially independent of the number of
people present in the scene. Trained on COCO data alone, our system achieves
COCO test-dev keypoint average precision of 0.665 using single-scale inference
and 0.687 using multi-scale inference, significantly outperforming all previous
bottom-up pose estimation systems. We are also the first bottom-up method to
report competitive results for the person class in the COCO instance
segmentation task, achieving a person category average precision of 0.417.
