2020/01/01 by Youssef Alami Mejjati, Mejjati, Youssef Alami, Zejiang Shen +11
Computer Science · Engineering · #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image Enhancement Techniques #Image and Video Processing (eess.IV) #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2001.02595
27 pages, 25 figures, 11 tables. Paper under review
openalex publication_date 2020/01/01 · arxiv created 2020/01/10 · arxiv updated 2020/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture. Given an object class, a user-provided bounding box, and a background image, we first use a mask generator to create an object shape, and then use a texture generator to fill the mask such that the texture integrates with the background. By separating the problem of object insertion into these two stages, we show that our model allows us to improve the realism of diverse object generation that also agrees with the provided background image. Our results on the challenging COCO dataset show improved overall quality and diversity compared to state-of-the-art object insertion approaches.