ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object
  Removal and Insertion
Daniel Winter Matan Cohen Shlomi Fruchter Yael Pritch Alex Rav-Acha Yedid Hoshen
Diffusion models have revolutionized image editing but often generate images
that violate physical laws, particularly the effects of objects on the scene,
e.g., occlusions, shadows, and reflections. By analyzing the limitations of
self-supervised approaches, we propose a practical solution centered on a
\q{counterfactual} dataset. Our method involves capturing a scene before and
after removing a single object, while minimizing other changes. By fine-tuning
a diffusion model on this dataset, we are able to not only remove objects but
also their effects on the scene. However, we find that applying this approach
for photorealistic object insertion requires an impractically large dataset. To
tackle this challenge, we propose bootstrap supervision; leveraging our object
removal model trained on a small counterfactual dataset, we synthetically
expand this dataset considerably. Our approach significantly outperforms prior
methods in photorealistic object removal and insertion, particularly at
modeling the effects of objects on the scene.