The goal of image composition is merging a foreground object into a
background image to obtain a realistic composite image. Recently, generative
composition methods are built on large pretrained diffusion models, due to
their unprecedented image generation ability. They train a model on abundant
pairs of foregrounds and backgrounds, so that it can be directly applied to a
new pair of foreground and background at test time. However, the generated
results often lose the foreground details and exhibit noticeable artifacts. In
this work, we propose an embarrassingly simple approach named DreamCom inspired
by DreamBooth. Specifically, given a few reference images for a subject, we
finetune text-guided inpainting diffusion model to associate this subject with
a special token and inpaint this subject in the specified bounding box. We also
construct a new dataset named MureCom well-tailored for this task.

图像合成的目标是将前景对象与背景图像合并以获取逼真的合成图像。最近，基于大型预训练扩散模型的生成式合成方法由于其前所未有的图像生成能力而得到发展。然而，生成的结果往往会丢失前景细节并出现明显的伪影。在本文中，我们提出了一种名为 DreamCom 的尴尬简单方法，灵感来自 DreamBooth。具体来说，给定一个主题的几个参考图像，我们通过微调文本导向修复扩散模型来将该主题与特殊标记相关联，并在指定的边界框内修复该主题。我们还构建了一个专门为此任务设计的新数据集 MureCom。