In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternatively, transformer networks with a sparse contrastive loss have been explored, but their over-relaxed regularization often leads to imprecise object placement. We introduce BOOTPLACE, a novel paradigm that formulates object placement as a placement-by-detection problem. Our approach begins by identifying suitable regions of interest for object placement. This is achieved by training a specialized detection transformer on object-subtracted backgrounds, enhanced with multi-object supervisions. It then semantically associates each target compositing object with detected regions based on their complementary characteristics. Through a boostrapped training approach applied to randomly object-subtracted images, our model enforces meaningful placements through extensive paired data augmentation. Experimental results on established benchmarks demonstrate BOOTPLACE's superior performance in object repositioning, markedly surpassing state-of-the-art baselines on Cityscapes and OPA datasets with notable improvements in IOU scores. Additional ablation studies further showcase the compositionality and generalizability of our approach, supported by user study evaluations.

翻译：本文针对复制-粘贴图像到图像合成问题，重点研究物体放置学习。先前的方法利用生成模型来减少对密集监督的依赖，但这往往限制了其建模复杂数据分布的能力。另一种方案是探索使用具有稀疏对比损失的Transformer网络，但其过度宽松的正则化通常导致物体放置不精确。我们提出了BOOTPLACE，一种将物体放置问题形式化为"通过检测进行放置"的新范式。我们的方法首先识别适合物体放置的兴趣区域，这是通过在物体被移除的背景上训练一个专门的检测Transformer来实现的，该训练过程辅以多物体监督。随后，模型根据互补特性，将每个目标合成物体与检测到的区域进行语义关联。通过对随机移除物体的图像应用自举式训练方法，我们的模型通过广泛的配对数据增强来强制执行有意义的放置。在现有基准测试上的实验结果表明，BOOTPLACE在物体重定位任务上表现优异，在Cityscapes和OPA数据集上显著超越了现有最先进的基线方法，并在IOU分数上取得了显著提升。额外的消融研究进一步展示了我们方法的组合性和泛化能力，用户研究评估也为此提供了支持。