Making a Case for Learning Motion Representations with Phase
arXiv:1609.01693
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.
ECCV 2016 Workshop on Brave new ideas for motion representations in videos
References in corpus (7)
- Two-Stream Convolutional Networks for Action Recognition in Videos
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
- Convolutional Two-Stream Network Fusion for Video Action Recognition
- Preserving Color in Neural Artistic Style Transfer
- VideoLSTM Convolves, Attends and Flows for Action Recognition
- Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification