FCPose
FCPose
**FCPose** is a fully convolutional multi-person [pose estimation framework](https://paperswithcode.com/methods/category/pose-estimation-models) using dynamic instance-aware convolutions. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping pre-processing with dynamic instance aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. 

Overall, FCPose is built upon the one-stage object detector [FCOS](https://paperswithcode.com/method/fcos). The controller that generates the weights of the keypoint heads is attached to the FCOS heads. The weights $\theta\_{i}$ generated by the controller is used to fulfill the keypoint head $f$ for the instance $i$. Moreover, a keypoint refinement module is introduced to predict the offsets from each location of the heatmaps to the ground-truth keypoints. Finally, the coordinates derived from the predicted heatmaps are refined by the offsets predicted by the keypoint refinement module, resulting in the final keypoint results. "Rel. coord." is a map of the relative coordinates from all the locations of the feature maps $F$ to the location where the weights are generated. The relative coordinate map is concatenated to $F$ as the input to the keypoint head.
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions
https://paperswithcode.com/paper/fcpose-fully-convolutional-multi-person-pose
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions