RORem: Training a Robust Object Remover with Human-in-the-Loop

TL;DR

This paper introduces RORem, a semi-supervised, human-in-the-loop approach for training a robust object remover that significantly improves removal success rates and image quality over previous methods.

Contribution

The paper presents a novel semi-supervised training strategy with human feedback to create high-quality paired data for object removal, enhancing model robustness and performance.

Findings

Object removal success rate improved by over 18%.

Created a dataset with over 200K high-quality pairs.

Achieved state-of-the-art results in reliability and image quality.

Abstract

Despite the significant advancements, existing object removal methods struggle with incomplete removal, incorrect content synthesis and blurry synthesized regions, resulting in low success rates. Such issues are mainly caused by the lack of high-quality paired training data, as well as the self-supervised training paradigm adopted in these methods, which forces the model to in-paint the masked regions, leading to ambiguity between synthesizing the masked objects and restoring the background. To address these issues, we propose a semi-supervised learning strategy with human-in-the-loop to create high-quality paired training data, aiming to train a Robust Object Remover (RORem). We first collect 60K training pairs from open-source datasets to train an initial object removal model for generating removal samples, and then utilize human feedback to select a set of high-quality object removal pairs, with which we train a discriminator to automate the following training data generation process. By iterating this process for several rounds, we finally obtain a substantial object removal dataset with over 200K pairs. Fine-tuning the pre-trained stable diffusion model with this dataset, we obtain our RORem, which demonstrates state-of-the-art object removal performance in terms of both reliability and image quality. Particularly, RORem improves the object removal success rate over previous methods by more than 18\%. The dataset, source code and trained model are available at https://github.com/leeruibin/RORem.