EG 2019 - Short Papers

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https://diglib.eg.org/handle/10.2312/2632750

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Browsing EG 2019 - Short Papers by Subject "Animation"

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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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    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.