Paint by Inpaint: Learning to Add Image Objects by Removing Them First
Authors: name: Navve Wasserman, name: Noam Rotstein, name: Roy Ganz, name: Ron Kimmel

Image editing has advanced significantly with the introduction of
text-conditioned diffusion models. Despite this progress, seamlessly adding
objects to images based on textual instructions without requiring user-provided
input masks remains a challenge. We address this by leveraging the insight that
removing objects (Inpaint) is significantly simpler than its inverse process of
adding them (Paint), attributed to the utilization of segmentation mask
datasets alongside inpainting models that inpaint within these masks.
Capitalizing on this realization, by implementing an automated and extensive
pipeline, we curate a filtered large-scale image dataset containing pairs of
images and their corresponding object-removed versions. Using these pairs, we
train a diffusion model to inverse the inpainting process, effectively adding
objects into images. Unlike other editing datasets, ours features natural
target images instead of synthetic ones; moreover, it maintains consistency
between source and target by construction. Additionally, we utilize a large
Vision-Language Model to provide detailed descriptions of the removed objects
and a Large Language Model to convert these descriptions into diverse,
natural-language instructions. We show that the trained model surpasses
existing ones both qualitatively and quantitatively, and release the
large-scale dataset alongside the trained models for the community.