FCPose: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions

Weian Mao, Zhi Tian, Xinlong Wang, Chunhua Shen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 9034-9043

Abstract


We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping post-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. Moreover, with the strong representation capacity of dynamic convolutions, the keypoint heads in FCPose are designed to be very compact, resulting in fast inference and makes FCPose have almost constant inference time regardless of the number of persons in the image. For example, on the COCO dataset, a real-time version of FCPose using the DLA-34 backbone infers about 4.5 times faster than Mask R-CNN (ResNet-101) (41.67 FPS vs. 9.26 FPS) while achieving improved performance (64.8% AP vs. 64.3% AP). FCPose also offers better speed/accuracy trade-off than other state-of-the-art methods. Our experiment results show that FCPose is a simple yet effective multi-person pose estimation framework. Code is available at: https://git.io/AdelaiDet

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Mao_2021_CVPR, author = {Mao, Weian and Tian, Zhi and Wang, Xinlong and Shen, Chunhua}, title = {FCPose: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {9034-9043} }