论文标题
Amico：Amodal实例组成
AMICO: Amodal Instance Composition
论文作者
论文摘要
图像组成旨在混合多个对象以形成统一的图像。现有的方法通常假定精确分段和完整的对象。但是，这种假设在不受约束的情况下很难满足。我们将Amodal实例组成介绍了不完美的组合 - 可能不完整和/或粗分段的对象 - 在目标图像上。我们首先开发对象形状预测和内容完成模块以综合Amodal内容。然后，我们提出了一个神经成分模型，以无缝混合对象。我们的主要技术新颖性在于使用单独的前景/背景表示并将掩盖预测融合到减轻细分错误。我们的结果表明，公共可可和亲戚基准的最先进表现，并在各种场景中取得了有利的视觉效果。我们演示了各种图像组成应用，例如对象插入和截断。
Image composition aims to blend multiple objects to form a harmonized image. Existing approaches often assume precisely segmented and intact objects. Such assumptions, however, are hard to satisfy in unconstrained scenarios. We present Amodal Instance Composition for compositing imperfect -- potentially incomplete and/or coarsely segmented -- objects onto a target image. We first develop object shape prediction and content completion modules to synthesize the amodal contents. We then propose a neural composition model to blend the objects seamlessly. Our primary technical novelty lies in using separate foreground/background representations and blending mask prediction to alleviate segmentation errors. Our results show state-of-the-art performance on public COCOA and KINS benchmarks and attain favorable visual results across diverse scenes. We demonstrate various image composition applications such as object insertion and de-occlusion.
