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Puppify - Automatic Generation of Planar Marionettes from Frontal Images
Elad Richardson and Gil Ben-Shachar
Supervised by Anastasia Dubrovina and Aaron Weltzer
In this project, we propose a method for fully automating the body segmentation process, thus enabling a wide variety of consumer and security applications and removing the friction caused by manual input. The process starts with a deep convolutional network, used to localize body joints, which are refined and stabilized using Reverse Ensembling and skin tone cues. The skeletal pose model is then exploited to create "auto-scribbles": automatically generated foreground/background scribble masks that can be used as inputs for a wide range of segmentation algorithms to directly extract the subject's body from the background. Simple segmentation aware cropping produces individual body part crops which can be used to generate a planar marionette for repositioning and animation.
Please, see project report.
Please, see final presentation.
Please, see demo clip.