Cross-view image synthesis using conditional GANs
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Learning to generate natural scenes has always been a challenging task in computer vision. It is even more painstaking when the generation is conditioned on images with drastically different views. This is mainly because understanding, corresponding, and transforming appearance and semantic information across the views is not trivial. In this paper, we attempt to solve the novel problem of cross-view image synthesis, aerial to street-view and vice versa, using conditional generative adversarial networks (cGAN). Two new architectures called Crossview Fork (X-Fork) and Crossview Sequential (X-Seq) are proposed to generate scenes with resolutions of 64 × 64 and 256 × 256 pixels. X-Fork architecture has a single discriminator and a single generator. The generator hallucinates both the image and its semantic segmentation in the target view. X-Seq architecture utilizes two cGANs. The first one generates the target image which is subsequently fed to the second cGAN for generating its corresponding semantic segmentation map. The feedback from the second cGAN helps the first cGAN generate sharper images. Both of our proposed architectures learn to generate natural images as well as their semantic segmentation maps. The proposed methods show that they are able to capture and maintain the true semantics of objects in source and target views better than the traditional image-to-image translation method which considers only the visual appearance of the scene. Extensive qualitative and quantitative evaluations support the effectiveness of our frameworks, compared to two state of the art methods, for natural scene generation across drastically different views.
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Conditional GANs alleviate mode collapse by adding a conditional vector to both the generator and the discriminator.
“Conditional GANs effectively alleviate the limitations with mode collapse by adding a conditional vector to both the generator and the discriminator (Regmi and Borji, 2018).”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Regmi and Borji, 2018
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A Deep-Learning-Based Surrogate Model for Thermal Signature Prediction in Laser Metal Deposition
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Local and Global GANs With Semantic-Aware Upsampling for Image Generation
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Simultaneous Direct Depth Estimation and Synthesis Stereo for Single Image Plant Root Reconstruction
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Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization
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Cross-View Exocentric to Egocentric Video Synthesis
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First- And Third-Person Video Co-Analysis By Learning Spatial-Temporal Joint Attention
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Image-To-Image Translation Using Pix2Pix GAN and Cycle GAN
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SemGeo: Semantic Keywords for Cross-View Image Geo-Localization
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AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network
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Egocentric Action Recognition by Automatic Relation Modeling
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Put Myself in Your Shoes: Lifting the Egocentric Perspective from Exocentric Videos
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From Third Person to First Person: Dataset and Baselines for Synthesis and RetrievalPreprint
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Self-citationBEV-SLAM: Building a Globally-Consistent World Map Using Monocular Vision
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Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
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360 Panorama Synthesis from a Sparse Set of Images with Unknown Field of View
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Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image
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ArcGeo: Localizing Limited Field-of-View Images using Cross-view Matching
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Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance
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Pose Guided Human Image Synthesis by View Disentanglement and Enhanced Weighting Loss
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VAGeo: View-specific Attention for Cross-View Object Geo-Localization
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A Recurrent Transformer Network for Novel View Action Synthesis
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Cross-View Contextual Relation Transferred Network for Unsupervised Vehicle Tracking in Drone Videos
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Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
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- Authors
- Regmi, K., & Borji, A.
- Affiliations
- University of Central Florida (US)
- ORCID
- Borji, Ali
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 3501-3510
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2792572768
- Cited by
- 204
- References
- 46
- Citations by year
- 2026: 11 · 2025: 26 · 2024: 25 · 2023: 24 · 2022: 31 · 2021: 28 · 2020: 37 · 2019: 12 · 2018: 10
- FWCI
- 6.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- conditional generative adversarial networks, semantic segmentation, image-to-image translation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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