Abstract
Although diffusion-based image genenation has been widely explored and applied, background generation tasks in e-commerce scenarios still face significant challenges. The first challenge is to ensure that the generated products are consistent with the given product inputs while maintaining a reasonable spatial arrangement, harmonious shadows, and reflections between foreground products and backgrounds. Existing inpainting methods fail to address this due to the lack of domain-specific data. The second challenge involves the limitation of relying solely on text prompts for image control, as effective integrating visual information to achieve precise control in inpainting tasks remains underexplored. To address these challenges, we introduce DreamEcom-400K, a high-quality e-commerce dataset containing accurate product instance masks, background reference images, text prompts, and aesthetically pleasing product images. Based on this dataset, we propose DreamPainter, a novel framework that not only utilizes text prompts for control but also flexibly incorporates reference image information as an additional control signal. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, maintaining high product consistency while effectively integrating both text prompt and reference image information.
Abstract (translated)
尽管基于扩散模型的图像生成技术已经被广泛研究和应用，但在电子商务场景中的背景生成任务仍然面临重大挑战。第一个挑战是确保生成的产品与给定的产品输入一致，同时保持合理的空间布局、和谐的阴影以及前景产品和背景之间的反射效果。现有的修复算法（inpainting methods）由于缺乏特定领域的数据而无法解决这个问题。第二个挑战则是依赖于仅使用文本提示进行图像控制的局限性，有效地将视觉信息整合以实现精确的修复任务控制仍是一个未被充分探索的问题。 为了解决这些挑战，我们引入了DreamEcom-400K，这是一个高质量的电子商务数据集，其中包含准确的产品实例掩码、背景参考图片、文本提示以及美观的产品图像。基于这个数据集，我们提出了DreamPainter，一种新的框架，它不仅利用文本提示进行控制，还灵活地将参考图信息作为额外的控制信号集成进来。 广泛的实验表明，我们的方法在保持高产品一致性的同时显著优于最先进的方法，并且能够有效地整合文本提示和参考图片的信息。
URL
https://arxiv.org/abs/2508.02155
