  In this paper, we research the new topic of object effects recommendation in
micro-video platforms, which is a challenging but important task for many
practical applications such as advertisement insertion. To avoid the problem of
introducing background bias caused by directly learning video content from
image frames, we propose to utilize the meaningful body language hidden in 3D
human pose for recommendation. To this end, in this work, a novel human pose
driven object effects recommendation network termed PoseRec is introduced.
PoseRec leverages the advantages of 3D human pose detection and learns
information from multi-frame 3D human pose for video-item registration,
resulting in high quality object effects recommendation performance. Moreover,
to solve the inherent ambiguity and sparsity issues that exist in object
effects recommendation, we further propose a novel item-aware implicit
prototype learning module and a novel pose-aware transductive hard-negative
mining module to better learn pose-item relationships. What's more, to
benchmark methods for the new research topic, we build a new dataset for object
effects recommendation named Pose-OBE. Extensive experiments on Pose-OBE
demonstrate that our method can achieve superior performance than strong
baselines.
