  We introduce YOLO-pose, a novel heatmap-free approach for joint detection,
and 2D multi-person pose estimation in an image based on the popular YOLO
object detection framework. Existing heatmap based two-stage approaches are
sub-optimal as they are not end-to-end trainable and training relies on a
surrogate L1 loss that is not equivalent to maximizing the evaluation metric,
i.e. Object Keypoint Similarity (OKS). Our framework allows us to train the
model end-to-end and optimize the OKS metric itself. The proposed model learns
to jointly detect bounding boxes for multiple persons and their corresponding
2D poses in a single forward pass and thus bringing in the best of both
top-down and bottom-up approaches. Proposed approach doesn't require the
postprocessing of bottom-up approaches to group detected keypoints into a
skeleton as each bounding box has an associated pose, resulting in an inherent
grouping of the keypoints. Unlike top-down approaches, multiple forward passes
are done away with since all persons are localized along with their pose in a
single inference. YOLO-pose achieves new state-of-the-art results on COCO
validation (90.2% AP50) and test-dev set (90.3% AP50), surpassing all existing
bottom-up approaches in a single forward pass without flip test, multi-scale
testing, or any other test time augmentation. All experiments and results
reported in this paper are without any test time augmentation, unlike
traditional approaches that use flip-test and multi-scale testing to boost
performance. Our training codes will be made publicly available at
https://github.com/TexasInstruments/edgeai-yolov5 and
https://github.com/TexasInstruments/edgeai-yolox
