Paper Info
Title
Creative Flow Plus Dataset
Abstract
We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth. Our dataset includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. Our dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size. While modern techniques for tasks such as optical flow estimation achieve impressive performance on realistic images and video, today there is no way to gauge their performance on non-photorealistic images. Creative Flow+ poses a new challenge to generalize real-world Computer Vision to messy stylized content. We show that learning-based optical flow methods fail to generalize to this data and struggle to compete with classical approaches, and invite new research in this area.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00553
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Maria Shugrina1201.66
Ziheng Liang200.34
David Acuna3685.19
Jiaman Li410.68
Angad Singh500.34
Karan Singh6152976.00
Sanja Fidler72087116.71