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
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This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer. The goal of this paper is to recognize actions in video without the need for examples. Different from traditional zero-shot approaches we do not demand the design and specification of attribute classifiers and class-to-attribute mappings to allow for transfer from seen classes to unseen classes. Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories. Action labels are assigned to an object encoding of unseen video based on a convex combination of action and object affinities. Our semantic embedding has three main characteristics to accommodate for the specifics of actions. First, we propose a mechanism to exploit multiple-word descriptions of actions and objects. Second, we incorporate the automated selection of the most responsive objects per action. And finally, we demonstrate how to extend our zero-shot approach to the spatio-temporal localization of actions in video. Experiments on four action datasets demonstrate the potential of our approach. Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the original image style transfer technique by based on energy minimization. We introduce new ways of initialization and new loss functions to generate consistent and stable stylized video sequences even in cases with large motion and strong occlusion. Our second approach formulates video stylization as a learning problem. We propose a deep network architecture and training procedures that allow us to stylize arbitrary-length videos in a consistent and stable way, and nearly in real time. We show that the proposed methods clearly outperform simpler baselines both qualitatively and quantitatively. Finally, we propose a way to adapt these approaches also to 360 (^ ) images and videos as they emerge with recent virtual reality hardware. Videos recorded on moving cameras are often known to be shaky due to unstable carrier motion and the video stabilization problem involves inferring the intended smooth motion to keep and the unintended shaky motion to remove. However, conventional methods typically require proper, scenario-specific parameter setting, which does not generalize well across different scenarios. Moreover, we observe that a stable video should satisfy two conditions: a smooth trajectory and consistent inter-frame transition. While conventional methods only target at the former condition, we address these two issues at the same time. In this paper, we propose a homography consistency based algorithm to directly extract the optimal smooth trajectory and evenly distribute the inter-frame transition. By optimizing in the homography domain, our method does not need further matrix decomposition and parameter adjustment, automatically adapting to all possible types of motion (eg. translational or rotational) and video properties (eg. frame rates). We test our algorithm on translational videos recorded from a car and rotational videos from a hovering aerial vehicle, both of high and low frame rates. Results show our method widely applicable to different scenarios without any need of additional parameter adjustment.