  How to frame (or crop) a photo often depends on the image subject and its
context; e.g., a human portrait. Recent works have defined the subject-aware
image cropping task as a nuanced and practical version of image cropping. We
propose a weakly-supervised approach (GenCrop) to learn what makes a
high-quality, subject-aware crop from professional stock images. Unlike
supervised prior work, GenCrop requires no new manual annotations beyond the
existing stock image collection. The key challenge in learning from this data,
however, is that the images are already cropped and we do not know what regions
were removed. Our insight is to combine a library of stock images with a
modern, pre-trained text-to-image diffusion model. The stock image collection
provides diversity and its images serve as pseudo-labels for a good crop, while
the text-image diffusion model is used to out-paint (i.e., outward inpainting)
realistic uncropped images. Using this procedure, we are able to automatically
generate a large dataset of cropped-uncropped training pairs to train a
cropping model. Despite being weakly-supervised, GenCrop is competitive with
state-of-the-art supervised methods and significantly better than comparable
weakly-supervised baselines on quantitative and qualitative evaluation metrics.
