  Human-object interactions (HOI) recognition and pose estimation are two
closely related tasks. Human pose is an essential cue for recognizing actions
and localizing the interacted objects. Meanwhile, human action and their
interacted objects' localizations provide guidance for pose estimation. In this
paper, we propose a turbo learning framework to perform HOI recognition and
pose estimation simultaneously. First, two modules are designed to enforce
message passing between the tasks, i.e. pose aware HOI recognition module and
HOI guided pose estimation module. Then, these two modules form a closed loop
to utilize the complementary information iteratively, which can be trained in
an end-to-end manner. The proposed method achieves the state-of-the-art
performance on two public benchmarks including Verbs in COCO (V-COCO) and
HICO-DET datasets.
