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.
Additionally, we also consider video-to-video transfer where the style of performing a certain action is transferred from a target video to the input video. In @cite_19 the authors allow the users to change the video by adding plausible object manipulations in the video. Similar to this work, we also want to change the video motion after the recording is done, by adjusting the style of the action being performed.
@cite_19
2084583166
We present algorithms for extracting an image-space representation of object structure from video and using it to synthesize physically plausible animations of objects responding to new, previously unseen forces. Our representation of structure is derived from an image-space analysis of modal object deformation: projections of an object's resonant modes are recovered from the temporal spectra of optical flow in a video, and used as a basis for the image-space simulation of object dynamics. We describe how to extract this basis from video, and show that it can be used to create physically-plausible animations of objects without any knowledge of scene geometry or material properties.