Video Motion Stylization by 2D Rigidification

International audienceThis paper introduces a video stylization method that increases the apparent rigidity of motion. Existing stylization methods often retain the 3D motion of the original video, making the result look like a 3D scene covered in paint rather than a 2D painting of a scene. In contrast, traditional hand-drawn animations often exhibit simplified in-plane motion, such as in the case of cutout animations where the animator moves pieces of paper from frame to frame. Inspired by this technique, we propose to modify a video such that its content undergoes 2D rigid transforms. To achieve this goal, our approach applies motion segmentation and optimization to best approximate the input optical flow with piecewise-rigid transforms, and re-renders the video such that its content follows the simplified motion. The output of our method is a new video and its optical flow, which can be fed to any existing video stylization algorithm

Proof of Concept For the Use of Motion Capture Technology In Athletic Pedagogy

Visualization has long been an important method for conveying complex information. Where information transfer using written and spoken means might amount to 200-250 words per minute, visual media can often convey information at many times this rate. This makes visualization a potentially important tool for education. Athletic instruction, particularly, can involve communication about complex human movement that is not easily conveyed with written or spoken descriptions. Video based instruction can be problematic since video data can contain too much information, thereby making it more difficult for a student to absorb what is cognitively necessary. The lesson is to present the learner what is needed and not more. We present a novel use of motion capture animation as an educational tool for teaching athletic movements. The advantage of motion capture is its ability to accurately represent real human motion in a minimalist context which removes extraneous information normally found in video. Motion capture animation only displays motion information, not additional information regarding the motion context. Producing an “automated coach” would be too large and difficult a problem to solve within the scope of a Master's thesis but we can perform initial steps including producing a useful software tool which performs data analysis on two motion datasets. We believe such a tool would be beneficial to a human coach as an analysis tool and the work would provide some useful understanding of next important steps towards perhaps someday producing an automated coach

Illustration non-photoréaliste de séquences de captures de mouvements

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal