通过在重新着色之前启用深度图像来插入剪贴对象
Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading
摘要：
我们将展示如何将一个对象从一个图像插入到另一个图像中，并在硬情况下获得逼真的结果，插入对象的着色与场景的着色发生冲突。使用场景的照明模型渲染对象是行不通的，因为这样做需要对象的几何和材质模型，而这很难从单个图像中恢复。在本文中，我们介绍了一种方法，纠正阴影不一致的插入对象，而不需要一个几何和物理模型或环境地图。我们的方法使用深度图像先验（DIP），通过一致的图像分解推理损失训练生成插入对象的重绘渲染。从DIP得到的图像旨在具有（a）类似于剪贴的阴影的阴影，（b）类似于目标场景的阴影，以及（c）与剪贴表面法线一致的阴影。其结果是一个简单的过程，产生令人信服的着色插入对象。我们展示了我们的方法的有效性，定性和定量的几个对象具有复杂的表面特性，也对数据集的球形灯罩进行定量评估。我们的方法显着优于所有这些对象的图像协调（IH）基线。在一项超过100名用户的用户研究中，它们的表现也优于剪贴和IH基线。
英文摘要：
We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene. Rendering objects using an illumination model of the scene doesn't work, because doing so requires a geometric and material model of the object, which is hard to recover from a single image. In this paper, we introduce a method that corrects shading inconsistencies of the inserted object without requiring a geometric and physical model or an environment map. Our method uses a deep image prior (DIP), trained to produce reshaded renderings of inserted objects via consistent image decomposition inferential losses. The resulting image from DIP aims to have (a) an albedo similar to the cut-and-paste albedo, (b) a similar shading field to that of the target scene, and (c) a shading that is consistent with the cut-and-paste surface normals. The result is a simple procedure that produces convincing shading of the inserted object. We show the efficacy of our method both qualitatively and quantitatively for several objects with complex surface properties and also on a dataset of spherical lampshades for quantitative evaluation. Our method significantly outperforms an Image Harmonization (IH) baseline for all these objects. They also outperform the cut-and-paste and IH baselines in a user study with over 100 users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
