Cited by 887Generative Adversarial Networks and Image SynthesisComputer Graphics and Visualization TechniquesAdvanced Neural Network Applications

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Photographic Image Synthesis with Cascaded Refinement Networks

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Chen, Qifeng, Koltun, Vladlen·2017·2017 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2017.168

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Abstract

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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Authors: Chen, Q., & Koltun, V.
Affiliations: Intel (United States) (US)
ORCID: Chen, Qifeng
Koltun, Vladlen
Venue: 2017 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2017-10
Pages: 1520-1529
DOI: 10.1109/iccv.2017.168
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

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Photographic Image Synthesis with Cascaded Refinement Networks

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