Paper Info
Title
End-To-End Time-Lapse Video Synthesis From A Single Outdoor Image
Abstract
Time-lapse videos usually contain visually appealing content but are often difficult and costly to create. In this paper, we present an end-to-end solution to synthesize a time-lapse video from a single outdoor image using deep neural networks. Our key idea is to train a conditional generative adversarial network based on existing datasets of time-lapse videos and image sequences. We propose a multi-frame joint conditional generation framework to effectively learn the correlation between the illumination change of an outdoor scene and the time of the day. We further present a multi-domain training scheme for robust training of our generative models from two datasets with different distributions and missing timestamp labels. Compared to alternative time-lapse video synthesis algorithms, our method uses the timestamp as the control variable and does not require a reference video to guide the synthesis of the final output. We conduct ablation studies to validate our algorithm and compare with state-of-the-art techniques both qualitatively and quantitatively.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00150
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Generative adversarial network,Computer science,End-to-end principle,Control variable,Timestamp,Artificial intelligence,Generative grammar,Machine learning,Deep neural networks
Journal
abs/1904.00680
ISSN
Citations 
PageRank 
1063-6919
5
0.41
References 
Authors
0
6
Name
Order
Citations
PageRank
Seonghyeon Nam1283.05
Chongyang Ma225719.21
Menglei Chai319114.24
William Brendel439615.12
Ning Xu518420.03
Seon Joo Kim645531.34