Prof. Dr. Jürgen Döllner


ViVid: Depicting Dynamics in Stylized Live Photos

Semmo, Amir; Reimann, Max; Klingbeil, Mandy; Shekhar, Sumit; Trapp, Matthias; Döllner, Jürgen in Proceedings SIGGRAPH Appy Hour Seite 8:1-8:2 . New York , ACM , 2019 .

We present ViVid, a mobile app for iOS that empowers users to express dynamics in stylized Live Photos. This app uses state-of-the-art computer-vision techniques based on convolutional neural networks to estimate motion in the video footage that is captured together with a photo. Based on these analytics and best practices of contemporary art, photos can be stylized as a pencil drawing or cartoon look that includes design elements to visually suggest motion, such as ghosts, motion lines and halos. Its interactive parameterizations enable users to filter and art-direct composition variables, such as color, size and opacity, of the stylization process. ViVid is based on Apple's CoreML, Metal and PhotoKit APIs for optimized on-device processing. Thus, the motion estimation is scheduled to utilize the dedicated neural engine and GPU in parallel, while shading-based image stylization is able to process the video footage in real-time. This way, the app provides a unique tool for creating lively photo stylizations with ease.
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