EG2022

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https://diglib.eg.org/handle/10.2312/2633163

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Browsing EG2022 by Subject "Animation"

Now showing 1 - 5 of 5
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    Neural Motion Compression with Frequency-adaptive Fourier Feature Network
    (The Eurographics Association, 2022) Tojo, Kenji; Chen, Yifei; Umetani, Nobuyuki; Pelechano, Nuria; Vanderhaeghe, David
    We present a neural-network-based compression method to alleviate the storage cost of motion capture data. Human motions such as locomotion, often consist of periodic movements. We leverage this periodicity by applying Fourier features to a multilayered perceptron network. Our novel algorithm finds a set of Fourier feature frequencies based on the discrete cosine transformation (DCT) of motion. During training, we incrementally added a dominant frequency of the DCT to a current set of Fourier feature frequencies until a given quality threshold was satisfied. We conducted an experiment using CMU motion dataset, and the results suggest that our method achieves overall high compression ratio while maintaining its quality.
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    Safeguarding our Dance Cultural Heritage
    (The Eurographics Association, 2022) Aristidou, Andreas; Chalmers, Alan; Chrysanthou, Yiorgos; Loscos, Celine; Multon, Franck; Parkins, J. E.; Sarupuri, Bhuvan; Stavrakis, Efstathios; Hahmann, Stefanie; Patow, Gustavo A.
    Folk dancing is a key aspect of intangible cultural heritage that often reflects the socio-cultural and political influences prevailing in different periods and nations; each dance produces a meaning, a story with the help of music, costumes and dance moves. It has been transmitted from generation to generation, and to different countries, mainly due to movements of people carrying and disseminating their civilization. However, folk dancing, amongst other intangible heritage, is at high risk of disappearing due to wars, the moving of populations, economic crises, modernization, but most importantly, because these fragile creations have been modified over time through the process of collective recreation, and/or changes in the way of life. In this tutorial, we show how the European Project, SCHEDAR, exploited emerging technologies to digitize, analyze, and holistically document our intangible heritage creations, that is a critical necessity for the preservation and the continuity of our identity as Europeans.
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    Splash in a Flash: Sharpness-aware Minimization for Efficient Liquid Splash Simulation
    (The Eurographics Association, 2022) Jetly, Vishrut; Ibayashi, Hikaru; Nakano, Aiichiro; Sauvage, Basile; Hasic-Telalovic, Jasminka
    We present sharpness-aware minimization (SAM) for fluid dynamics which can efficiently learn the plausible dynamics of liquid splashes. Due to its ability to achieve robust and generalizing solutions, SAM efficiently converges to a parameter set that predicts plausible dynamics of elusive liquid splashes. Our training scheme requires 6 times smaller number of epochs to converge and, 4 times shorter wall-clock time. Our result shows that sharpness of loss function has a close connection to the plausibility of fluid dynamics and suggests further applicability of SAM to machine learning based fluid simulation.
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    Stroke based Painterly Inbetweening
    (The Eurographics Association, 2022) Barroso, Nicolas; Fondevilla, Amélie; Vanderhaeghe, David; Sauvage, Basile; Hasic-Telalovic, Jasminka
    Creating a 2D animation with visible strokes is a tedious and time consuming task for an artist. Computer aided animation usually focus on cartoon stylized rendering, or is built from an automatic process as 3D animations stylization, loosing the painterly look and feel of hand made animation. We propose to simplify the creation of stroke-based animations: from a set of key frames, our methods automatically generates intermediate frames to depict the animation. Each intermediate frame looks as it could have been drawn by an artist, using the same high level stroke based representation as key frame, and in succession they display the subtle temporal incoherence usually found in hand-made animations.
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    A Survey on Reinforcement Learning Methods in Character Animation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Kwiatkowski, Ariel; Alvarado, Eduardo; Kalogeiton, Vicky; Liu, C. Karen; Pettré, Julien; Panne, Michiel van de; Cani, Marie-Paule; Meneveaux, Daniel; Patanè, Giuseppe
    Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take actions based on their observation of the environment, and receive appropriate rewards which define the objective. This experience is then used to progressively improve the policy controlling the agent's behavior, typically represented by a neural network. This trained module can then be reused for similar problems, which makes this approach promising for the animation of autonomous, yet reactive characters in simulators, video games or virtual reality environments. This paper surveys the modern Deep Reinforcement Learning methods and discusses their possible applications in Character Animation, from skeletal control of a single, physically-based character to navigation controllers for individual agents and virtual crowds. It also describes the practical side of training DRL systems, comparing the different frameworks available to build such agents.