Data-driven hallucination of different times of day from a single outdoor photo
Author(s)Shih, YiChang; Paris, Sylvain; Durand, Fredo; Freeman, William T.
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
ACM Transactions on Graphics
Association for Computing Machinery
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.
Author's final manuscript