Miguel Ramos Carretero

CGI DH2323 31st of March, 2014

Computer Sciences (Madrid) First approach to CG (Shortfilm)

Exchange student (Stockholm) LandSeed (CGI project)

Master Thesis (in progress)

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Song vs Song (2012) [1] LandSeed v1.0 (2013) [2]

Virtual characters Perception

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Character model: Stickman

Online database of CG models

TurboSquid [3]

Mixamo [4]

Some of them free and already rigged!

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Animation: Motion Capture Library (MoCap)

Online database of MoCap: Carnegie-Mellon Graphics Lab Motion Capture Database [5] Free to use

2548 human motions

3dsMax-friendly

MoCap - The Lord of the Rings (2001) [6]

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( Example )

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Crowd: Unity (Game Engine)

Walkers / Standing groups

GraphMesh

A* Pathfinding [7]

KTH model

Behaviour

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( Demo )

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Second part of the Master Thesis: Study about visual perception of virtual characters

Eye-tracker [8]

Experiment: Short videos

Questionnaire

About 30 minutes

Participants are needed! Eye-tracker devices - Tobii ® [8]

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Human interest: Films / Games (i.e. uncanny valley, realistic behaviour, …)

Psychology

Robotics

Storytelling

…

“Art challenges technology,

technology inspires art”

[John Lasseter - Pixar]

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Computer Graphics is much more than just

nice renderings!

[1] Song vs Song – Shortfilm: http://youtu.be/S4EVcqv5JzI [2] LandSeed v1.0 DemoReel: http://youtu.be/W4C0U5p2K3k [3] TurboSquid webpage: http://www.turbosquid.com/ (Last time accessed: 30/03/2013) [4] Mixamo webpage: http://www.mixamo.com/ (Last time accessed: 30/03/2013) [5] Carnegie Mellon University Motion Capture Database: http://mocap.cs.cmu.edu/ (Last time accessed: 30/03/2013) [6] The Lord of the Rings (film series), Peter Jackson. New Line Cinema, 2001-2003. [7] A* Pathfinding project: http://arongranberg.com/astar/ (Last time accessed: 30/03/2013) [8] Tobii Technologies: http://www.tobii.com/

About me:

Miguel Ramos Carretero

Master Student in Computer Sciences at Kungliga Tekniska Högskolan (Stockholm)

e-mail: miguelrc@kth.se

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Perceptual experiment: Studying the mood of a scene with virtual agents

About 30 minutes

Sign up at the end of this class!