Painting many pasts: Synthesizing time lapse videos of paintings
Name
2001.01026.pdf
Description
Submitted version
Size
5.39 MB
Format
Adobe PDF
Checksum (MD5)
b66105ea23cf87e5e46972e0a1d737e0
Author(s)
Zhao, Amy (Xiaoyu Amy)
Balakrishnan, Guha
Lewis, Kathleen M.(Kathleen Marie)
Durand, Frederic
Guttag, John V
Dalca, Adrian Vasile
Date Issued
June 2020
Journal
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Citation
Zhao, Amy et al. “Painting many pasts: Synthesizing time lapse videos of paintings.” Paper in the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, 13-19 June 2020, IEEE: 8789–8797 © 2020 The Author(s)
Version
Original manuscript
Abstract
We introduce a new video synthesis task: synthesizing time lapse videos depicting how a given painting might have been created. Artists paint using unique combinations of brushes, strokes, and colors. There are often many possible ways to create a given painting. Our goal is to learn to capture this rich range of possibilities. Creating distributions of long-term videos is a challenge for learning-based video synthesis methods. We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process. We implement this model as a convolutional neural network, and introduce a novel training scheme to enable learning from a limited dataset of painting time lapses. We demonstrate that this model can be used to sample many time steps, enabling long-term stochastic video synthesis. We evaluate our method on digital and watercolor paintings collected from video websites, and show that human raters find our synthetic videos to be similar to time lapse videos produced by real artists. Our code is available at https://xamyzhao.github.io/timecraft.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR42600.2020.00846
