Photographic Image Synthesis with Cascaded Refinement Networks
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We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches.
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This opens interesting research opportunities for the adaptation of lab-generated simulation to real-world conditions, similar to what has been observed in image synthesis for self driving and robotics.
“This opens interesting research opportunities for the adaptation of lab-generated simulation to real-world conditions, similar to what has been observed in the field of image synthesis for self driving143 and robotics144 in the last decade.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [143]
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Single Image Reflection Separation with Perceptual Losses
Zhang, X., Ng, R., & Chen, Q. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00503 · Cited by 396 · see citing works
Self-citationBBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network
Fan, D.-P., Zhai, Y., Borji, A., Yang, J., & Shao, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58610-2_17 · Cited by 394 · see citing works
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Liu, M., Ding, Y., Xia, M., Liu, X., Ding, E., Zuo, W., & Wen, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00379 · Cited by 394 · see citing works
Towards Universal Fake Image Detectors that Generalize Across Generative Models
Ojha, U., Li, Y., & Lee, Y. J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02345 · Cited by 428 · see citing works
Perceptual Adversarial Networks for Image-to-Image Transformation
Wang, C., Xu, C., Wang, C., & Tao, D. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836316 · Cited by 384 · see citing works
Fast Image Processing with Fully-Convolutional Networks
Chen, Q., Xu, J., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.273 · Cited by 358 · see citing works
Self-citationSketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
Chen, W., & Hays, J. H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00981 · Cited by 342 · see citing works
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
Hong, S., Yang, D., Choi, J., & Lee, H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00833 · Cited by 348 · see citing works
Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network
Zhang, Z., Xie, Y., & Yang, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00649 · Cited by 331 · see citing works
Pushing the Boundaries of View Extrapolation With Multiplane Images
Srinivasan, P. P., Tucker, R. P., Barron, J. T., Ramamoorthi, R., Ng, R., & Snavely, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00026 · Cited by 325 · see citing works
Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 326 · see citing works
Deep exemplar-based colorization
He, M., Chen, D., Liao, J., Sander, P. V., & Yuan, L. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201365 · Cited by 299 · see citing works
Stoll, S., Camgoz, N. C., Hadfield, S., & Bowden, R. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01281-2 · Cited by 314 · see citing works
DeepLPF: Deep Local Parametric Filters for Image Enhancement
Moran, S., Marza, P., McDonagh, S., Parisot, S., & Slabaugh, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01284 · Cited by 296 · see citing works
ManiGAN: Text-Guided Image Manipulation
Li, B., Qi, X., Lukasiewicz, T., & Torr, P. H. S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00790 · Cited by 287 · see citing works
Zhang, X., Chen, Q., Ng, R., & Koltun, V. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00388 · Cited by 277 · see citing works
Self-citationText-based Editing of Talking-head Video
Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323028 · Cited by 270 · see citing works
Hu, R., Huang, Z., Tang, Y., Kaick, O. V., Zhang, H., & Huang, H. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392391 · Cited by 288 · see citing works
Single-image SVBRDF capture with a rendering-aware deep network
Deschaintre, V., Aittala, M., Durand, F., George Drettakis, B., & Bousseau, A. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201378 · Cited by 274 · see citing works
Riegler, G., & Koltun, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_37 · Cited by 270 · see citing works
Self-citationGeometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Fu, H., Gong, M., Wang, C.-H., Batmanghelich, K. N., Zhang, K., & Tao, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00253 · Cited by 261 · see citing works
Cross-Domain Correspondence Learning for Exemplar-Based Image Translation
Zhang, P., Zhang, B., Chen, D., Yuan, L., & Wen, F. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00519 · Cited by 255 · see citing works
Li, Z., Shafiei, M., Ramamoorthi, R., Sunkavalli, K., & Chandraker, M. K. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00255 · Cited by 247 · see citing works
Dual Contrastive Learning for Unsupervised Image-to-Image Translation
Han, J., Shoeiby, M., Petersson, L., & Armin, M. A. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00084 · Cited by 245 · see citing works
Swapping Autoencoder for Deep Image ManipulationPreprint
Park, T., Zhu, J.-Y., Wang, O., Lu, J., Shechtman, E., Efros, A. A., & Zhang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00653 · Cited by 253 · see citing works
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow
Ilg, E., Çiçek, Ö., Galesso, S., Klein, A., Makansi, O., Hutter, F., & Brox, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_40 · Cited by 223 · see citing works
Interactive Image Segmentation with Latent Diversity
Li, Z., Chen, Q., & Koltun, V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00067 · Cited by 218 · see citing works
Self-citationNeverova, N., Güler, R. A., & Kokkinos, I. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_8 · Cited by 218 · see citing works
Shen, Z., Lai, W., Xu, T., Kautz, J., & Yang, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00862 · Cited by 218 · see citing works
Adversarial text-to-image synthesis: A review
Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.019 · Cited by 214 · see citing works
A survey of image synthesis and editing with generative adversarial networks
Wu, X., Xu, K., & Hall, P. M. (2017) · Tsinghua Science & Technology · doi:10.23919/tst.2017.8195348 · Cited by 201 · see citing works
You Said That?: Synthesising Talking Faces from Audio
Jamaludin, A., Chung, J. S., & Zisserman, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01150-y · Cited by 189 · see citing works
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191 · see citing works
“The methods such as style transfer (Chen et al., 2018c), (Yao et al., 2019), image inpainting (Yu et al., 2018), (Yu et al., 2018), rendering (Chen and Koltun, 2017), (Park et al., 2019), (Liu et al., 2021a), super resolution (Dai et al., 2019), (Guo et al., 2020b), (Liu et al., 2020a), (Mei et…”
MethodMakansi, O., Ilg, E., Çiçek, Ö., & Brox, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00731 · Cited by 193 · see citing works
Attention-GAN for Object Transfiguration in Wild Images
Chen, X., Xu, C., Yang, X., & Tao, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_11 · Cited by 188 · see citing works
One Shot Face Swapping on Megapixels
Zhu, Y., Li, Q., Wang, J., Xu, C., & Sun, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00480 · Cited by 189 · see citing works
Riegler, G., & Koltun, V. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01204 · Cited by 181 · see citing works
Self-citationUnsupervised Domain-Specific Deblurring via Disentangled Representations
Lu, B., Chen, J.-C., & Chellappa, R. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01047 · Cited by 176 · see citing works
Connecting Vision and Language with Localized Narratives
Pont-Tuset, J., Uijlings, J., Changpinyo, S., Soricut, R., & Ferrari, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_38 · Cited by 176 · see citing works
SeGAN: Segmenting and Generating the Invisible
Ehsani, K., Mottaghi, R., & Farhadi, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00643 · Cited by 175 · see citing works
Zhang, L., Li, C., Wong, T., Ji, Y., & Liu, C. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275090 · Cited by 169 · see citing works
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation
Lambert, J., Liu, Z., Şener, O., Hays, J. H., & Koltun, V. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00295 · Cited by 176 · see citing works
Self-citationNatarajan, B., Rajalakshmi, E., Elakkiya, R., Kotecha, K. V., Abraham, A., Gabralla, L. A., & Subramaniyaswamy, V. (2022) · IEEE Access · doi:10.1109/access.2022.3210543 · Cited by 175 · see citing works
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
Sha, Z., Li, Z., Yu, N., & Zhang, Y. (2023) · Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3576915.3616588 · Cited by 175 · see citing works
GAN-based synthetic brain PET image generation
Islam, J., & Zhang, Y. (2020) · Brain Informatics · doi:10.1186/s40708-020-00104-2 · Cited by 172 · see citing works
Bifurcated Backbone Strategy for RGB-D Salient Object Detection
Zhai, Y., Fan, D.-P., Yang, J., Borji, A., Shao, L., Han, J., & Wang, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3116793 · Cited by 171 · see citing works
Speech Denoising with Deep Feature Losses
Germain, F. G., Chen, Q., & Koltun, V. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1924 · Cited by 169 · see citing works
Self-citationTang, H., Xu, D., Yan, Y., Torr, P. H. S., & Sebe, N. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00789 · Cited by 166 · see citing works
Semi-Parametric Image Synthesis
Qi, X., Chen, Q., Jia, J., & Koltun, V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00918 · Cited by 166 · see citing works
Self-citationConditional Infilling GANs for Data Augmentation in Mammogram Classification
Wu, E., Wu, K., Cox, D., & Lotter, W. E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00946-5_11 · Cited by 162 · see citing works
LayoutVAE: Stochastic Scene Layout Generation From a Label Set
Jyothi, A. A., Durand, T., He, J., Sigal, L., & Mori, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00999 · Cited by 153 · see citing works
Semantic Object Accuracy for Generative Text-to-Image Synthesis
Hinz, T., Heinrich, S., & Wermter, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3021209 · Cited by 152 · see citing works
Image-to-image Translation via Hierarchical Style Disentanglement
Li, X., Zhang, S., Hu, J., Cao, L., Hong, X., Mao, X., Huang, F., Wu, Y., & Ji, R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00853 · Cited by 161 · see citing works
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
Alldieck, T., Zanfir, M., & Sminchisescu, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00156 · Cited by 151 · see citing works
Stereo Magnification: Learning View Synthesis using Multiplane Images
Zhou, T., Tucker, R., Flynn, J., Fyffe, G., & Snavely, N. (2018) · arXiv · doi:10.48550/arxiv.1805.09817 · Cited by 150 · see citing works
StyleGAN2 Distillation for Feed-Forward Image Manipulation
Viazovetskyi, Y., Ivashkin, V., & Kashin, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_11 · Cited by 149 · see citing works
DocUNet: Document Image Unwarping via a Stacked U-Net
Ma, K., Shu, Z., Bai, X., Wang, J., & Samaras, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00494 · Cited by 150 · see citing works
SketchyCOCO: Image Generation From Freehand Scene Sketches
Gao, C., Liu, Q., Xu, Q., Wang, L., Liu, J., & Zou, C. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00522 · Cited by 147 · see citing works
- Authors
- Chen, Q., & Koltun, V.
- Affiliations
- Intel (United States) (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1520-1529
- OpenAlex
- W2963522749
- Cited by
- 887
- References
- 62
- Citations by year
- 2026: 13 · 2025: 40 · 2024: 51 · 2023: 88 · 2022: 117 · 2021: 170 · 2020: 145 · 2019: 156 · 2018: 94 · 2017: 12
- FWCI
- 32.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- semantic segmentation, feedforward neural networks, high-resolution image generation, realistic image synthesis, outdoor scenes, indoor scenes
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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