Abstract
Human pose estimation still faces various difficulties in
challenging scenarios. Human parsing, as a closely related
task, can provide valuable cues for better pose estimation,
which however has not been fully exploited. In this paper, we
propose a novel Parsing Induced Learner to exploit parsing
information to effectively assist pose estimation by learning
to fast adapt the base pose estimation model. The proposed
Parsing Induced Learner is composed of a parsing encoder
and a pose model parameter adapter, which together learn
to predict dynamic parameters of the pose model to extract
complementary useful features for more accurate pose estimation. Comprehensive experiments on benchmarks LIP
and extended PASCAL-Person-Part show that the proposed
Parsing Induced Learner can improve performance of both
single- and multi-person pose estimation to new state-of-theart. Cross-dataset experiments also show that the proposed
Parsing Induced Learner from LIP dataset can accelerate
learning of a human pose estimation model on MPII benchmark in addition to achieving outperforming performance