Computer Science > Computer Vision and Pattern Recognition
arXiv:2409.06074 (cs)
[Submitted on 9 Sep 2024]
Title:SVS-GAN: Leveraging GANs for Semantic Video Synthesis
View a PDF of the paper titled SVS-GAN: Leveraging GANs for Semantic Video Synthesis, by Khaled M. Seyam and 3 other authors
View PDFHTML (experimental)Abstract:In recent years, there has been a growing interest in Semantic Image Synthesis (SIS) through the use of Generative Adversarial Networks (GANs) and diffusion models. This field has seen innovations such as the implementation of specialized loss functions tailored for this task, diverging from the more general approaches in Image-to-Image (I2I) translation. While the concept of Semantic Video Synthesis (SVS)$\unicode{x2013}$the generation of temporally coherent, realistic sequences of images from semantic maps$\unicode{x2013}$is newly formalized in this paper, some existing methods have already explored aspects of this field. Most of these approaches rely on generic loss functions designed for video-to-video translation or require additional data to achieve temporal coherence. In this paper, we introduce the SVS-GAN, a framework specifically designed for SVS, featuring a custom architecture and loss functions. Our approach includes a triple-pyramid generator that utilizes SPADE blocks. Additionally, we employ a U-Net-based network for the image discriminator, which performs semantic segmentation for the OASIS loss. Through this combination of tailored architecture and objective engineering, our framework aims to bridge the existing gap between SIS and SVS, outperforming current state-of-the-art models on datasets like Cityscapes and KITTI-360.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2409.06074 [cs.CV] |
| (orarXiv:2409.06074v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2409.06074 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled SVS-GAN: Leveraging GANs for Semantic Video Synthesis, by Khaled M. Seyam and 3 other authors
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