  Pose estimation plays a critical role in human-centered vision applications.
However, it is difficult to deploy state-of-the-art HRNet-based pose estimation
models on resource-constrained edge devices due to the high computational cost
(more than 150 GMACs per frame). In this paper, we study efficient architecture
design for real-time multi-person pose estimation on edge. We reveal that
HRNet's high-resolution branches are redundant for models at the
low-computation region via our gradual shrinking experiments. Removing them
improves both efficiency and performance. Inspired by this finding, we design
LitePose, an efficient single-branch architecture for pose estimation, and
introduce two simple approaches to enhance the capacity of LitePose, including
Fusion Deconv Head and Large Kernel Convs. Fusion Deconv Head removes the
redundancy in high-resolution branches, allowing scale-aware feature fusion
with low overhead. Large Kernel Convs significantly improve the model's
capacity and receptive field while maintaining a low computational cost. With
only 25% computation increment, 7x7 kernels achieve +14.0 mAP better than 3x3
kernels on the CrowdPose dataset. On mobile platforms, LitePose reduces the
latency by up to 5.0x without sacrificing performance, compared with prior
state-of-the-art efficient pose estimation models, pushing the frontier of
real-time multi-person pose estimation on edge. Our code and pre-trained models
are released at https://github.com/mit-han-lab/litepose.
