Publication

Physics-based Reconstruction and Animation of Humans

Alexandru Eugen Ichim
2017
EPFL thesis
Abstract

Creating digital representations of humans is of utmost importance for applications ranging from entertainment (video games, movies) to human-computer interaction and even psychiatrical treatments. What makes building credible digital doubles difficult is the fact that the human vision system is very sensitive to perceiving the complex expressivity and potential anomalies in body structures and motion. This thesis will present several projects that tackle these problems from two different perspectives: lightweight acquisition and physics-based simulation. It starts by describing a complete pipeline that allows users to reconstruct fully rigged 3D facial avatars using video data coming from a handheld device (e.g., smartphone). The avatars use a novel two-scale representation composed of blendshapes and dynamic detail maps. They are constructed through an optimization that integrates feature tracking, optical flow, and shape from shading. Continuing along the lines of accessible acquisition systems, we discuss a framework for simultaneous tracking and modeling of articulated human bodies from RGB-D data. We show how semantic information can be extracted from the scanned body shapes. In the second half of the thesis, we will deviate from using standard linear reconstruction and animation models, and rather focus on exploiting physics-based techniques that are able to incorporate complex phenomena such as dynamics, collision response and incompressibility of the materials. The first approach we propose assumes that each 3D scan of an actor records his body in a physical steady state and uses a process called inverse physics to extract a volumetric physics-ready anatomical model of him. By using biologically-inspired growth models for the bones, muscles and fat, our method can obtain realistic anatomical reconstructions that can be later on animated using external tracking data such as the one resulting from tracking motion capture markers. This is then extended to a novel physics-based approach for facial reconstruction and animation. We propose a facial animation model which simulates biomechanical muscle contractions in a volumetric head model in order to create the facial expressions seen in the input scans. We then show how this approach allows for new avenues of dynamic artistic control, simulation of corrective facial surgery, and interaction with external forces and objects.

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Facial motion capture is the process of electronically converting the movements of a person's face into a digital database using cameras or laser scanners. This database may then be used to produce computer graphics (CG), computer animation for movies, games, or real-time avatars. Because the motion of CG characters is derived from the movements of real people, it results in a more realistic and nuanced computer character animation than if the animation were created manually.
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Animation is the method by which are manipulated to create moving images. In traditional animation, images are drawn or painted by hand on transparent celluloid sheets to be photographed and exhibited on film. Today, many animations are computer animations made with (CGI). Stop motion animation, in particular claymation, has continued to exist alongside these other forms. Animation is contrasted with live-action film, although the two do not exist in isolation. Many moviemakers have produced films that are a hybrid of the two.
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