3,614 research outputs found
Modeling variation of human motion
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 groĂen 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 groĂen 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. SchlieĂlich 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 groĂe 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
Reactive Statistical Mapping: Towards the Sketching of Performative Control with Data
Part 1: Fundamental IssuesInternational audienceThis paper presents the results of our participation to the ninth eNTERFACE workshop on multimodal user interfaces. Our target for this workshop was to bring some technologies currently used in speech recognition and synthesis to a new level, i.e. being the core of a new HMM-based mapping system. The idea of statistical mapping has been investigated, more precisely how to use Gaussian Mixture Models and Hidden Markov Models for realtime and reactive generation of new trajectories from inputted labels and for realtime regression in a continuous-to-continuous use case. As a result, we have developed several proofs of concept, including an incremental speech synthesiser, a software for exploring stylistic spaces for gait and facial motion in realtime, a reactive audiovisual laughter and a prototype demonstrating the realtime reconstruction of lower body gait motion strictly from upper body motion, with conservation of the stylistic properties. This project has been the opportunity to formalise HMM-based mapping, integrate various of these innovations into the Mage library and explore the development of a realtime gesture recognition tool
Classification of human motion based on affective state descriptors
Cataloged from PDF version of article.Human body movements and postures carry emotion-specific information. On the basis of this motivation, the objective of
this study is to analyze this information in the spatial and temporal structure of the motion capture data and extract features
that are indicative of certain emotions in terms of affective state descriptors. Our contribution comprises identifying the
directly or indirectly related descriptors to emotion classification in human motion and conducting a comprehensive analysis
of these descriptors (features) that fall into three different categories: posture descriptors, dynamic descriptors, and
frequency-based descriptors in order to measure their performance with respect to predicting the affective state of an input
motion. The classification results demonstrate that no single category is sufficient by itself; the best prediction performance
is achieved when all categories are combined. Copyright © 2013 John Wiley & Sons, Ltd
Data-driven techniques for animating virtual characters
One of the key goals of current research in data-driven computer animation is the synthesis of new motion sequences from existing motion data. This thesis presents three novel techniques for synthesising the motion of a virtual character from existing motion data and develops a framework of solutions to key character animation problems.
The first motion synthesis technique presented is based on the characterâs locomotion composition process. This technique examines the ability of synthesising a variety of characterâs locomotion behaviours while easily specified constraints (footprints) are placed in the three-dimensional space. This is achieved by analysing existing motion data, and by assigning the locomotion behaviour transition process to transition graphs that are responsible for providing information about this process.
However, virtual characters should also be able to animate according to different style variations. Therefore, a second technique to synthesise real-time style variations of characterâs motion. A novel technique is developed that uses correlation between two different motion styles, and by assigning the motion synthesis process to a parameterised maximum a posteriori (MAP) framework retrieves the desire style content of the input motion in real-time, enhancing the realism of the new synthesised motion sequence.
The third technique presents the ability to synthesise the motion of the characterâs fingers either oâ”-line or in real-time during the performance capture process. The advantage of both techniques is their ability to assign the motion searching process to motion features. The presented technique is able to estimate and synthesise a valid motion of the characterâs fingers, enhancing the realism of the input motion.
To conclude, this thesis demonstrates that these three novel techniques combine in to a framework that enables the realistic synthesis of virtual character movements, eliminating the post processing, as well as enabling fast synthesis of the required motion
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
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 ânaturalâ) 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
Audio is all in one: speech-driven gesture synthetics using WavLM pre-trained model
The generation of co-speech gestures for digital humans is an emerging area
in the field of virtual human creation. Prior research has made progress by
using acoustic and semantic information as input and adopting classify method
to identify the person's ID and emotion for driving co-speech gesture
generation. However, this endeavour still faces significant challenges. These
challenges go beyond the intricate interplay between co-speech gestures, speech
acoustic, and semantics; they also encompass the complexities associated with
personality, emotion, and other obscure but important factors. This paper
introduces "diffmotion-v2," a speech-conditional diffusion-based and
non-autoregressive transformer-based generative model with WavLM pre-trained
model. It can produce individual and stylized full-body co-speech gestures only
using raw speech audio, eliminating the need for complex multimodal processing
and manually annotated. Firstly, considering that speech audio not only
contains acoustic and semantic features but also conveys personality traits,
emotions, and more subtle information related to accompanying gestures, we
pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract
low-level and high-level audio information. Secondly, we introduce an adaptive
layer norm architecture in the transformer-based layer to learn the
relationship between speech information and accompanying gestures. Extensive
subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT
datasets to confirm the WavLM and the model's ability to synthesize natural
co-speech gestures with various styles.Comment: 10 pages, 5 figures, 1 tabl
Neural Dynamic Movement Primitives -- a survey
One of the most important challenges in robotics is producing accurate
trajectories and controlling their dynamic parameters so that the robots can
perform different tasks. The ability to provide such motion control is closely
related to how such movements are encoded. Advances on deep learning have had a
strong repercussion in the development of novel approaches for Dynamic Movement
Primitives. In this work, we survey scientific literature related to Neural
Dynamic Movement Primitives, to complement existing surveys on Dynamic Movement
Primitives
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