60,049 research outputs found

    Modeling variation of human motion

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    The synthesis of realistic human motion with large variations and different styles has a growing interest in simulation applications such as the game industry, psychological experiments, and ergonomic analysis. The statistical generative models are used by motion controllers in our motion synthesis framework to create new animations for different scenarios. Data-driven motion synthesis approaches are powerful tools for producing high-fidelity character animations. With the development of motion capture technologies, more and more motion data are publicly available now. However, how to efficiently reuse a large amount of motion data to create new motions for arbitrary scenarios poses challenges, especially for unsupervised motion synthesis. This thesis presents a series of works that analyze and model the variations of human motion data. The goal is to learn statistical generative models to create any number of new human animations with rich variations and styles. The work of the thesis will be presented in three main chapters. We first explore how variation is represented in motion data. Learning a compact latent space that can expressively contain motion variation is essential for modeling motion data. We propose a novel motion latent space learning approach that can intrinsically tackle the spatialtemporal properties of motion data. Secondly, we present our Morphable Graph framework for human motion modeling and synthesis for assembly workshop scenarios. A series of studies have been conducted to apply statistical motion modeling and synthesis approaches for complex assembly workshop use cases. Learning the distribution of motion data can provide a compact representation of motion variations and convert motion synthesis tasks to optimization problems. Finally, we show how the style variations of human activities can be modeled with a limited number of examples. Natural human movements display a rich repertoire of styles and personalities. However, it is difficult to get enough examples for data-driven approaches. We propose a conditional variational autoencoder (CVAE) to combine large variations in the neutral motion database and style information from a limited number of examples.Die Synthese realistischer menschlicher Bewegungen mit großen Variationen und unterschiedlichen Stilen ist fĂŒr Simulationsanwendungen wie die Spieleindustrie, psychologische Experimente und ergonomische Analysen von wachsendem Interesse. Datengetriebene BewegungssyntheseansĂ€tze sind leistungsstarke Werkzeuge fĂŒr die Erstellung realitĂ€tsgetreuer Charakteranimationen. Mit der Entwicklung von Motion-Capture-Technologien sind nun immer mehr Motion-Daten öffentlich verfĂŒgbar. Die effiziente Wiederverwendung einer großen Menge von Motion-Daten zur Erstellung neuer Bewegungen fĂŒr beliebige Szenarien stellt jedoch eine Herausforderung dar, insbesondere fĂŒr die unĂŒberwachte Bewegungssynthesemethoden. Das Lernen der Verteilung von Motion-Daten kann eine kompakte ReprĂ€sentation von Bewegungsvariationen liefern und Bewegungssyntheseaufgaben in Optimierungsprobleme umwandeln. In dieser Dissertation werden eine Reihe von Arbeiten vorgestellt, die die Variationen menschlicher Bewegungsdaten analysieren und modellieren. Das Ziel ist es, statistische generative Modelle zu erlernen, um eine beliebige Anzahl neuer menschlicher Animationen mit reichen Variationen und Stilen zu erstellen. In unserem Bewegungssynthese-Framework werden die statistischen generativen Modelle von Bewegungscontrollern verwendet, um neue Animationen fĂŒr verschiedene Szenarien zu erstellen. Die Arbeit in dieser Dissertation wird in drei Hauptkapiteln vorgestellt. Wir untersuchen zunĂ€chst, wie Variation in Bewegungsdaten dargestellt wird. Das Erlernen eines kompakten latenten Raums, der Bewegungsvariationen ausdrucksvoll enthalten kann, ist fĂŒr die Modellierung von Bewegungsdaten unerlĂ€sslich. Wir schlagen einen neuartigen Ansatz zum Lernen des latenten Bewegungsraums vor, der die rĂ€umlich-zeitlichen Eigenschaften von Bewegungsdaten intrinsisch angehen kann. Zweitens stellen wir unser Morphable Graph Framework fĂŒr die menschliche Bewegungsmodellierung und -synthese fĂŒr Montage-Workshop- Szenarien vor. Es wurde eine Reihe von Studien durchgefĂŒhrt, um statistische Bewegungsmodellierungs und syntheseansĂ€tze fĂŒr komplexe AnwendungsfĂ€lle in MontagewerkstĂ€tten anzuwenden. Schließlich zeigen wir anhand einer begrenzten Anzahl von Beispielen, wie die Stilvariationen menschlicher AktivitĂ€ten modelliertwerden können. NatĂŒrliche menschliche Bewegungen weisen ein reiches Repertoire an Stilen und Persönlichkeiten auf. Es ist jedoch schwierig, genĂŒgend Beispiele fĂŒr datengetriebene AnsĂ€tze zu erhalten. Wir schlagen einen Conditional Variational Autoencoder (CVAE) vor, um große Variationen in der neutralen Bewegungsdatenbank und Stilinformationen aus einer begrenzten Anzahl von Beispielen zu kombinieren. Wir zeigen, dass unser Ansatz eine beliebige Anzahl von natĂŒrlich aussehenden Variationen menschlicher Bewegungen mit einem Ă€hnlichen Stil wie das Ziel erzeugen kann

    Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

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    Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

    RGB-D-based Action Recognition Datasets: A Survey

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    Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols

    Substructure and Boundary Modeling for Continuous Action Recognition

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    This paper introduces a probabilistic graphical model for continuous action recognition with two novel components: substructure transition model and discriminative boundary model. The first component encodes the sparse and global temporal transition prior between action primitives in state-space model to handle the large spatial-temporal variations within an action class. The second component enforces the action duration constraint in a discriminative way to locate the transition boundaries between actions more accurately. The two components are integrated into a unified graphical structure to enable effective training and inference. Our comprehensive experimental results on both public and in-house datasets show that, with the capability to incorporate additional information that had not been explicitly or efficiently modeled by previous methods, our proposed algorithm achieved significantly improved performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure
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