  Multi-person pose estimation is an attractive and challenging task. Existing
methods are mostly based on two-stage frameworks, which include top-down and
bottom-up methods. Two-stage methods either suffer from high computational
redundancy for additional person detectors or they need to group keypoints
heuristically after predicting all the instance-agnostic keypoints. The
single-stage paradigm aims to simplify the multi-person pose estimation
pipeline and receives a lot of attention. However, recent single-stage methods
have the limitation of low performance due to the difficulty of regressing
various full-body poses from a single feature vector. Different from previous
solutions that involve complex heuristic designs, we present a simple yet
effective solution by employing instance-aware dynamic networks. Specifically,
we propose an instance-aware module to adaptively adjust (part of) the network
parameters for each instance. Our solution can significantly increase the
capacity and adaptive-ability of the network for recognizing various poses,
while maintaining a compact end-to-end trainable pipeline. Extensive
experiments on the MS-COCO dataset demonstrate that our method achieves
significant improvement over existing single-stage methods, and makes a better
balance of accuracy and efficiency compared to the state-of-the-art two-stage
approaches. The code and models are available at
\url{https://github.com/hikvision-research/opera}.
