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Photographic Image Synthesis with Cascaded Refinement Networks
Open accessAbstract
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 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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“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…”
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Metadata
- 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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