  Compositing-aware object search aims to find the most compatible objects for
compositing given a background image and a query bounding box. Previous works
focus on learning compatibility between the foreground object and background,
but fail to learn other important factors from large-scale data, i.e. geometry
and lighting. To move a step further, this paper proposes GALA
(Geometry-and-Lighting-Aware), a generic foreground object search method with
discriminative modeling on geometry and lighting compatibility for open-world
image compositing. Remarkably, it achieves state-of-the-art results on the CAIS
dataset and generalizes well on large-scale open-world datasets, i.e. Pixabay
and Open Images. In addition, our method can effectively handle non-box
scenarios, where users only provide background images without any input
bounding box. A web demo (see supplementary materials) is built to showcase
applications of the proposed method for compositing-aware search and automatic
location/scale prediction for the foreground object.
