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. Experiments are conducted on real-world images to showcase applications of the proposed method for compositing-aware search and automatic location/scale prediction for the foreground object.
CITATION STYLE
Zhu, S., Lin, Z., Cohen, S., Kuen, J., Zhang, Z., & Chen, C. (2022). GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13687 LNCS, pp. 676–692). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19812-0_39
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