论文标题
晚会：朝着几何形状和光明的对象搜索合成
GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing
论文作者
论文摘要
组合感知的对象搜索旨在找到给定背景图像和查询边界框的最兼容的对象。以前的作品着重于学习前景对象和背景之间的兼容性，但无法从大规模数据（即几何和照明）中学习其他重要因素。为了更进一步，本文提出了晚会（几何和光明感），这是一种通用前景对象搜索方法，具有关于几何形状和照明兼容性的开放世界图像组合的歧视性建模。值得注意的是，它在CAIS数据集上实现了最新的结果，并在大规模开放世界数据集（即Pixabay和Open Images）上很好地概括了。此外，我们的方法可以有效地处理非盒子方案，其中用户仅提供背景图像而没有任何输入边界框。 Web演示（请参阅补充材料）构建，以展示提出的用于组合感知搜索的方法和前景对象的自动位置/比例预测。
Compositing-aware object search aims to find the most compatible objects for compositing given a background image and a query bounding box. Previous works focus on learning compatibility between the foreground object and background, but fail to learn other important factors from large-scale data, i.e. geometry and lighting. To move a step further, this paper proposes GALA (Geometry-and-Lighting-Aware), a generic foreground object search method with discriminative modeling on geometry and lighting compatibility for open-world image compositing. Remarkably, it achieves state-of-the-art results on the CAIS dataset and generalizes well on large-scale open-world datasets, i.e. Pixabay and Open Images. In addition, our method can effectively handle non-box scenarios, where users only provide background images without any input bounding box. A web demo (see supplementary materials) is built to showcase applications of the proposed method for compositing-aware search and automatic location/scale prediction for the foreground object.
