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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.

Project Puppify - Automatic Generation of Planar Marionettes from Frontal Images Picture 1
Project Report

Please, see project report.

Final Presentation

Please, see final presentation.


Please, see demo clip.

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