CG2Real: Improving the Realism of Computer Generated Images using a Collection of Photographs
Name
Freeman_CG2Real improving.pdf
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4.84 MB
Format
Adobe PDF
Checksum (MD5)
4d70d38d9aef02c7b551728389dd5611
Author(s)
Johnson, Micah K.
Dale, Kevin
Avidan, Shai
Pfister, Hanspeter
Freeman, William T.
Matusik, Wojciech
Date Issued
September 2011
Journal
IEEE Transactions on Visualization and Computer Graphics
Publisher
Institute of Electrical and Electronics Engineers
Citation
Johnson, Micah K. et al. “CG2Real: Improving the Realism of Computer Generated Images Using a Large Collection of Photographs.” IEEE Transactions on Visualization and Computer Graphics 17 (2011): 1273-1285. © 2011 IEEE.
Version
Final published version
Abstract
Computer-generated (CG) images have achieved high levels of realism. This realism, however, comes at the cost of long and expensive manual modeling, and often humans can still distinguish between CG and real images. We introduce a new data-driven approach for rendering realistic imagery that uses a large collection of photographs gathered from online repositories. Given a CG image, we retrieve a small number of real images with similar global structure. We identify corresponding regions between the CG and real images using a mean-shift cosegmentation algorithm. The user can then automatically transfer color, tone, and texture from matching regions to the CG image. Our system only uses image processing operations and does not require a 3D model of the scene, making it fast and easy to integrate into digital content creation workflows. Results of a user study show that our hybrid images appear more realistic than the originals.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tvcg.2010.233
