Data-driven hallucination of different times of day from a single outdoor photo
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Author(s)
Shih, YiChang
Paris, Sylvain
Durand, Fredo
Freeman, William T.
Date Issued
November 2013
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery
Citation
Shih, Yichang, Sylvain Paris, Frédo Durand, and William T. Freeman. “Data-Driven Hallucination of Different Times of Day from a Single Outdoor Photo.” ACM Transactions on Graphics 32, no. 6 (November 1, 2013): 1–11.
Version
Author's final manuscript
Abstract
We introduce "time hallucination": synthesizing a plausible image at a different time of day from an input image. This challenging task often requires dramatically altering the color appearance of the picture. In this paper, we introduce the first data-driven approach to automatically creating a plausible-looking photo that appears as though it were taken at a different time of day. The time of day is specified by a semantic time label, such as "night".
Our approach relies on a database of time-lapse videos of various scenes. These videos provide rich information about the variations in color appearance of a scene throughout the day. Our method transfers the color appearance from videos with a similar scene as the input photo. We propose a locally affine model learned from the video for the transfer, allowing our model to synthesize new color data while retaining image details. We show that this model can hallucinate a wide range of different times of day. The model generates a large sparse linear system, which can be solved by off-the-shelf solvers. We validate our methods by synthesizing transforming photos of various outdoor scenes to four times of interest: daytime, the golden hour, the blue hour, and nighttime.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2508363.2508419
