AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (17.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks

University of Michigan, Ann Arbor, MI 48109 USA.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.
Show Author Information

Abstract

We introduce ShadowGAN, a generative adversarial network (GAN) for synthesizing shadows for virtual objects inserted in images. Given a target image containing several existing objects with shadows, and an input source object with a specified insertionposition, the network generates a realistic shadow for the source object. The shadow is synthesized by a generator; using the proposed local adversarial and global adversarial discriminators, the synthetic shadow’s appearance is locally realistic in shape, and globally consistent with other objects’ shadows in terms of shadow direction and area. To overcome the lack of training data, we produced training samples based on public 3D models and rendering technology. Experimental results from a user study show that the synthetic shadowed results look natural and authentic.

Keywords

shadow synthesisdeep learninggenera-tive adversarial networksimage synthesis

References

【1】
【1】
 
 
Crossref Google Scholar
Computational Visual Media
Volume 5 Issue 1,
March 2019
Pages 105-115
DOI: 10.1007/s41095-019-0136-1

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
Zhang S, Liang R, Wang M. ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks. Computational Visual Media, 2019, 5(1): 105-115. https://doi.org/10.1007/s41095-019-0136-1

1607

Views

130

Downloads

62

Crossref

N/A

Web of Science

69

Scopus

0

CSCD
Revised: 28 December 2018
Accepted: 05 February 2019
Published: 08 April 2019
© The Author(s) 2019

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from thecopyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.