A deep convolutional neural network for multi-frame video interpolation. Based on the work of Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller and Jan Kautz. To see the original work, please see:
H. Jiang, D. Sun, V. Jampani, M.-H. Yang, E. Learned-Miller and J. Kautz, "Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation," Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018, pp. 9000-9008.
or go to: arXiv:1712.00080
download_dataset.sh downloads two datasets, the Adobe 240-fps dataset and the Need for Speed dataset, that can be used for training the neural network. For more information on these datasets, please see:
S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich and O. Wang, "Deep Video Deblurring for Hand-Held Cameras," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 237-246.
or go to: arXiv:1611.08387
H. K. Galoogahi, A. Fagg, C. Huang, D. Ramanan and Simon Lucey, "Need for Speed: A Benchmark for Higher Frame Rate Object Tracking," Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 1125-1134.
or go to: arXiv:1703.05884
In order to download both datasets with the script, just type the following command in a terminal:
path_to_folder is the directory where the dataset should be downloaded. This directory is created if it does not exist.
The loss function includes a term that depends on the activations of a convolutional layer of the VGG16 neural network. The pretrained weights of the VGG16 neural networks are stored in the
vgg16_weights_no_fc.npz file. This file is a modified version of the file that can be found in this link. In order to reduce the size of the file, the weights of the fully connected layers have been removed. For more information on the VGG16 neural network, please see:
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA, 2015, pp. 1-14.
or go to: arXiv:1409.1556