EG2019

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https://diglib.eg.org/handle/10.2312/2632746

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Browsing EG2019 by Subject "Animation"

Now showing 1 - 4 of 4
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    Do contests improve students skills in Computer Graphics? The case of API8
    (The Eurographics Association, 2019) Palus, Jean-Pascal; Belhadj, Farès; Bourdin, Jean-Jacques; Tarini, Marco and Galin, Eric
    This paper presents a contest designed to improve the skills of students in Computer Graphics. The contest is adapted to the current skills of the students and uses a public graphic library. Students then have to produce a demo, generally a program which presents an animation. The main result presented in this paper is that with an appropriate set of tools, students program interesting demos to participate in the contest and their skills in Computer Graphics seem to improve significantly.
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    Perceptual Characteristics by Motion Style Category
    (The Eurographics Association, 2019) Kim, Hye Ji; Lee, Sung-Hee; Cignoni, Paolo and Miguel, Eder
    Motion style is important as it characterizes a motion by expressing the context of the motion such as emotion and personality. Yet, the perception and interpretation of motion styles is subjective and may vary greatly from person to person. This paper investigates the perceptual characteristics of motion styles for a wide range of styles. After categorizing the motion styles, we perform user studies to examine the diversity of interpretations of motion styles and the association level between style motions and their corresponding text descriptions. Our study shows that motion styles have different interpretation diversity and association level according to their categories. We discuss the implications of these findings and recommend a method of labeling or describing motion styles.
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    Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids
    (The Eurographics Association, 2019) Koschier, Dan; Bender, Jan; Solenthaler, Barbara; Teschner, Matthias; Jakob, Wenzel and Puppo, Enrico
    Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization technique, commonly used for the numerical simulation of continuum mechanical problems such as the simulation of fluids, highly viscous materials, and deformable solids. Recent advances in the field have made it possible to efficiently simulate massive scenes with highly complex boundary geometries on a single PC [Com16b, Com16a]. Moreover, novel techniques allow to robustly handle interactions among various materials [Com18,Com17]. As of today, graphics-inspired pressure solvers, neighborhood search algorithms, boundary formulations, and other contributions often serve as core components in commercial software for animation purposes [Nex17] as well as in computer-aided engineering software [FIF16]. This tutorial covers various aspects of SPH simulations. Governing equations for mechanical phenomena and their SPH discretizations are discussed. Concepts and implementations of core components such as neighborhood search algorithms, pressure solvers, and boundary handling techniques are presented. Implementation hints for the realization of SPH solvers for fluids, elastic solids, and rigid bodies are given. The tutorial combines the introduction of theoretical concepts with the presentation of actual implementations.
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    Stylistic Locomotion Modeling with Conditional Variational Autoencoder
    (The Eurographics Association, 2019) Du, Han; Herrmann, Erik; Sprenger, Janis; Cheema, Noshaba; hosseini, somayeh; Fischer, Klaus; Slusallek, Philipp; Cignoni, Paolo and Miguel, Eder
    We propose a novel approach to create generative models for distinctive stylistic locomotion synthesis. The approach is inspired by the observation that human styles can be easily distinguished from a few examples. However, learning a generative model for natural human motions which display huge amounts of variations and randomness would require a lot of training data. Furthermore, it would require considerable efforts to create such a large motion database for each style. We propose a generative model to combine the large variation in a neutral motion database and style information from a limited number of examples. We formulate the stylistic motion modeling task as a conditional distribution learning problem. Style transfer is implicitly applied during the model learning process. A conditional variational autoencoder (CVAE) is applied to learn the distribution and stylistic examples are used as constraints. We demonstrate that our approach can generate any number of natural-looking human motions with a similar style to the target given a few style examples and a neutral motion database.