@inproceedings{5f86396e5d754661af58d0d4a08739cf,
title = "Making a Case for Learning Motion Representations with Phase",
abstract = "This work advocates Eulerian motion representation learning over the current standard Lagrangian optical flow model. Eulerian motion is well captured by using phase, as obtained by decomposing the image through a complex-steerable pyramid. We discuss the gain of Eulerian motion in a set of practical use cases: (i) action recognition, (ii) motion prediction in static images, (iii) motion transfer in static images and, (iv) motion transfer in video. For each task we motivate the phase-based direction and provide a possible approach.",
author = "Silvia Pintea and {van Gemert}, Jan",
year = "2016",
doi = "10.1007/978-3-319-49409-8_8",
language = "English",
isbn = "978-3-319-49408-1",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "55--64",
editor = "G. Hua and H. Jegou",
booktitle = "Computer Vision ECCV 2016 Workshops",
edition = "3",
note = "ECCV 2016 : 29th European Conference on Computer Vision ; Conference date: 08-10-2016 Through 16-10-2016",
}