  This paper studies amodal image segmentation: predicting entire object
segmentation masks including both visible and invisible (occluded) parts. In
previous work, the amodal segmentation ground truth on real images is usually
predicted by manual annotaton and thus is subjective. In contrast, we use 3D
data to establish an automatic pipeline to determine authentic ground truth
amodal masks for partially occluded objects in real images. This pipeline is
used to construct an amodal completion evaluation benchmark, MP3D-Amodal,
consisting of a variety of object categories and labels. To better handle the
amodal completion task in the wild, we explore two architecture variants: a
two-stage model that first infers the occluder, followed by amodal mask
completion; and a one-stage model that exploits the representation power of
Stable Diffusion for amodal segmentation across many categories. Without bells
and whistles, our method achieves a new state-of-the-art performance on Amodal
segmentation datasets that cover a large variety of objects, including COCOA
and our new MP3D-Amodal dataset. The dataset, model, and code are available at
https://www.robots.ox.ac.uk/~vgg/research/amodal/.
