CG2Real: Improving the Realism of Computer Generated Images using a Collection of Photographs

Publication

IEEE Transactions on Visualization and Computer Graphics 17 (2011): 1273-1285

Authors

Micah K Johnson, Kevin Dale, Shai Avidan, Hanspeter Pfister, William T Freeman, Wojciech Matusik

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.

Paper

cg2real-improving-the-realism-of-computer-generated-images-using-a-collection-of-photographs.pdf