Abstract
In web data, product images are central to boosting user engagement and advertising efficacy on e-commerce platforms, yet the intrusive elements such as watermarks and promotional text remain major obstacles to delivering clear and appealing product visuals. Although diffusion-based inpainting methods have advanced, they still face challenges in commercial settings due to unreliable object removal and limited domain-specific adaptation. To tackle these challenges, we propose Repainter, a reinforcement learning framework that integrates spatial-matting trajectory refinement with Group Relative Policy Optimization (GRPO). Our approach modulates attention mechanisms to emphasize background context, generating higher-reward samples and reducing unwanted object insertion. We also introduce a composite reward mechanism that balances global, local, and semantic constraints, effectively reducing visual artifacts and reward hacking. Additionally, we contribute EcomPaint-100K, a high-quality, large-scale e-commerce inpainting dataset, and a standardized benchmark EcomPaint-Bench for fair evaluation. Extensive experiments demonstrate that Repainter significantly outperforms state-of-the-art methods, especially in challenging scenes with intricate compositions. We will release our code and weights upon acceptance.
Abstract (translated)
在网页数据中，产品图片对于提高电子商务平台上的用户参与度和广告效果至关重要。然而，水印和促销文字等侵入性元素仍然是提供清晰、吸引人的产品视觉效果的主要障碍。尽管基于扩散的图像修复方法已经有所进步，但在商业环境中仍然面临着对象移除不可靠以及领域特定适应能力有限的问题。为了应对这些挑战，我们提出了Repainter框架，这是一种结合了空间-遮罩轨迹细化与组相对策略优化（GRPO）的强化学习框架。我们的方法通过调节注意力机制来强调背景信息，从而生成高奖励样本并减少不需要的对象插入。此外，我们引入了一种复合奖励机制，该机制平衡全局、局部和语义约束，有效减少了视觉伪影和奖励欺骗。 另外，我们还贡献了一个高质量的大规模电子商务图像修复数据集EcomPaint-100K以及一个标准化基准测试平台EcomPaint-Bench，以实现公正的评估。广泛的实验表明，Repainter在具有复杂构图的挑战性场景中显著优于现有的最先进的方法。一旦获得接受，我们将发布我们的代码和权重。
URL
https://arxiv.org/abs/2510.07721
