Introducción a Blender

El mundo de la animación 3D está en plena actualidad en este momento. Salas de cine, programas de televisión y la publicidad nos muestran constantemente personajes y objetos animados creados por ordenador. Son muchos los programas que pueden usarse para la realización de animación en 3D. En este proyecto vamos a centrarnos en Blender. Blender es un programa de animación y modelado que se puede obtener de manera gratuita por internet. Se trata de un programa de software libre, por lo que constantemente está siendo modificado gracias a la aportación de usuarios anónimos por internet. El objetivo de este proyecto es la creación de un corto de animación de un minuto de duración con Blender, para lo cual serán necesarias una serie de pautas iniciales sobre cómo funciona Blender y sus posibilidades. La primera parte de este proyecto es una guía básica sobre Blender y sus características. Capítulo a capítulo se irán describiendo la interfaz del programa y la creación de objetos (modelado, asignación de materiales y texturas) para luego aprender a animarlos y a visualizarlos como imágenes o video. La segunda parte se centra en el corto MOL. Creado a partir de los conocimientos adquiridos en la guía anterior, añadiendo en algunos casos, nuevas propiedades necesarias para su elaboración. Se describirán cada uno de los elementos y su creación. Esta guía pretende ser un referente para aquel que quiera introducirse en el mundo de la animación 3D con Blender. The world of 3D animation is a trending topic nowadays. Cinema, television and advertising constantly show us characters and animated objects created with computer graphics. There are many programs that can be used to perform 3D animations. In this project we will focus on Blender. Blender is a modeling and animation program that is available for free online. Blender is an open source program, so it is constantly being modified and improved by anonymous online users. The objective of this project is to create a one-minute animation short with Blender, for which we will require an initial set of guidelines on how Blender works and its possibilities. The first part of this project is a basic guide and will only cover basic features of Blender. In each chapter we will describe the interface and how to create objects (modeling, assigning materials and textures) and then we will learn to animate these objects and to display them as images or video. The second part focuses on the short film MOL. Created from the knowledge gained in the previous guide adding, in same cases, new properties necessary for its creation. We will describe each of the elements involved in the making of. This guide is intended to be a referent guide for anyone who wants to enter the world of 3D animation with Blender.

Text categorization methods for automatic estimation of verbal intelligence

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.

Estimating Adaptacion of Dialogue Partners with Different Verbal Intelligence

This work investigates to what degree speakers with different verbal intelligence may adapt to each other. The work is based on a corpus consisting of 100 descriptions of a short film (monologues), 56 discussions about the same topic (dialogues), and verbal intelligence scores of the test participants. Adaptation between two dialogue partners was measured using cross-referencing, proportion of "I", "You" and "We" words, between-subject correlation and similarity of texts. It was shown that lower verbal intelligence speakers repeated more nouns and adjectives from the other and used the same linguistic categories more often than higher verbal intelligence speakers. In dialogues between strangers, participants with higher verbal intelligence showed a greater level of adaptation.

Investigating verbal intelligence using the TF-IDF approach

In this paper we investigated differences in language use of speakers yielding different verbal intelligence when they describe the same event. The work is based on a corpus containing descriptions of a short film and verbal intelligence scores of the speakers. For analyzing the monologues and the film transcript, the number of reused words, lemmas, n-grams, cosine similarity and other features were calculated and compared to each other for different verbal intelligence groups. The results showed that the similarity of monologues of higher verbal intelligence speakers was greater than of lower and average verbal intelligence participants. A possible explanation of this phenomenon is that candidates yielding higher verbal intelligence have a better short-term memory. In this paper we also checked a hypothesis that differences in vocabulary of speakers yielding different verbal intelligence are sufficient enough for good classification results. For proving this hypothesis, the Nearest Neighbor classifier was trained using TF-IDF vocabulary measures. The maximum achieved accuracy was 92.86%.