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COMPUTER SCIENCE > COMPUTER VISION AND PATTERN RECOGNITION

arXiv:2105.04619 (cs)
[Submitted on 10 May 2021]


TITLE:ENHANCING PHOTOREALISM ENHANCEMENT

Authors:Stephan R. Richter, Hassan Abu AlHaija, Vladlen Koltun
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> Abstract: We present an approach to enhancing the realism of synthetic images.
> The images are enhanced by a convolutional network that leverages intermediate
> representations produced by conventional rendering pipelines. The network is
> trained via a novel adversarial objective, which provides strong supervision
> at multiple perceptual levels. We analyze scene layout distributions in
> commonly used datasets and find that they differ in important ways. We
> hypothesize that this is one of the causes of strong artifacts that can be
> observed in the results of many prior methods. To address this we propose a
> new strategy for sampling image patches during training. We also introduce
> multiple architectural improvements in the deep network modules used for
> photorealism enhancement. We confirm the benefits of our contributions in
> controlled experiments and report substantial gains in stability and realism
> in comparison to recent image-to-image translation methods and a variety of
> other baselines.

Comments: Code and data available at this https URL Video available at this
https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial
Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG) ACM classes:
I.4.8 Cite as: arXiv:2105.04619 [cs.CV]   (or arXiv:2105.04619v1 [cs.CV] for
this version)   https://doi.org/10.48550/arXiv.2105.04619
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arXiv-issued DOI via DataCite


SUBMISSION HISTORY

From: Stephan R Richter [view email]
[v1] Mon, 10 May 2021 19:00:49 UTC (35,377 KB)

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