  Image cropping aims to find visually appealing crops in an image, which is an
important yet challenging task. In this paper, we consider a specific and
practical application: human-centric image cropping, which focuses on the
depiction of a person. To this end, we propose a human-centric image cropping
method with two novel feature designs for the candidate crop: partition-aware
feature and content-preserving feature. For partition-aware feature, we divide
the whole image into nine partitions based on the human bounding box and treat
different partitions in a candidate crop differently conditioned on the human
information. For content-preserving feature, we predict a heatmap indicating
the important content to be included in a good crop, and extract the geometric
relation between the heatmap and a candidate crop. Extensive experiments
demonstrate that our method can perform favorably against state-of-the-art
image cropping methods on human-centric image cropping task. Code is available
at https://github.com/bcmi/Human-Centric-Image-Cropping.
