Human motion modeling and simulation by anatomical approach

To instantly generate desired infinite realistic human motion is still a great challenge in virtual human simulation. In this paper, the novel emotion effected motion classification and anatomical motion classification are presented, as well as motion capture and parameterization methods. The framework for a novel anatomical approach to model human motion in a HTR (Hierarchical Translations and Rotations) file format is also described. This novel anatomical approach in human motion modelling has the potential to generate desired infinite human motion from a compact motion database. An architecture for the real-time generation of new motions is also propose

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 ‘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

Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance

Dance as a complex expressive form of motion is able to convey emotion, meaning and social idiosyncrasies that opens channels for non-verbal communication, and promotes rich cross-modal interactions with music and the environment. As such, realistic dancing characters may incorporate crossmodal information and variability of the dance forms through compact representations that may describe the movement structure in terms of its spatial and temporal organization. In this paper, we propose a novel method for synthesizing beatsynchronous dancing motions based on a compact topological model of dance styles, previously captured with a motion capture system. The model was based on the Topological Gesture Analysis (TGA) which conveys a discrete three-dimensional point-cloud representation of the dance, by describing the spatiotemporal variability of its gestural trajectories into uniform spherical distributions, according to classes of the musical meter. The methodology for synthesizing the modeled dance traces back the topological representations, constrained with definable metrical and spatial parameters, into complete dance instances whose variability is controlled by stochastic processes that considers both TGA distributions and the kinematic constraints of the body morphology. In order to assess the relevance and flexibility of each parameter into feasibly reproducing the style of the captured dance, we correlated both captured and synthesized trajectories of samba dancing sequences in relation to the level of compression of the used model, and report on a subjective evaluation over a set of six tests. The achieved results validated our approach, suggesting that a periodic dancing style, and its musical synchrony, can be feasibly reproduced from a suitably parametrized discrete spatiotemporal representation of the gestural motion trajectories, with a notable degree of compression

Fast and deep deformation approximations

Character rigs are procedural systems that compute the shape of an animated character for a given pose. They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance. When comparing film-quality character rigs with those designed for real-time applications, there is typically a substantial and readily apparent difference in the quality of the mesh deformations. Real-time rigs are limited by a computational budget and often trade realism for performance. Rigs for film do not have this same limitation, and character riggers can make the rig as complicated as necessary to achieve realistic deformations. However, increasing the rig complexity slows rig evaluation, and the animators working with it can become less efficient and may experience frustration. In this paper, we present a method to reduce the time required to compute mesh deformations for film-quality rigs, allowing better interactivity during animation authoring and use in real-time games and applications. Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions. The linear deformations are computed directly from the transformations of the rig's underlying skeleton. We use deep learning methods to approximate the remaining nonlinear portion. In the examples we show from production rigs used to animate lead characters, our approach reduces the computational time spent on evaluating deformations by a factor of 5×-10×. This significant savings allows us to run the complex, film-quality rigs in real-time even when using a CPU-only implementation on a mobile device

Generating 3D product design models in real-time using hand motion and gesture

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.Three dimensional product design models are widely used in conceptual design and in the early stage of prototyping during the design processes. A product design specification often demands a substantial amount of 3D models to be constructed within a short period of time. Current methods begin with designers sketching product concepts in 2D using pencil and paper, which in turn are then translated into 3D models by a design individual with CAD expertise, using a 3D modelling software package such as Pro Engineer, Solid Works, Auto CAD etc. Several novel methods have been used to incorporate hand motion as a way of interacting with computers. There are three main types of technology available to capture motion data, capable of translating human motion into numeric data which can be read by a computer system. The first being, hand gesture glove-based systems such as “Cyberglove”, these systems are generally used to capture hand gesture and joint angle information. The second is full body motion capture systems, optical and non-optical-based, and finally vision based gesture recognition systems which capture full degree of - freedom (DOF) hand motion estimation. There has yet to be a method using any of the above mentioned input devices to rapidly produce 3D product design models in real time, using hand motion and gestures. In this research, a novel method is presented, using a motion capture system to capture hand gestures and motion in real time, to recreate 3D curves and surfaces, which can be translated into 3D product design models. The main aim of this research is to develop a hand motion and gesture-based rapid 3D product modelling method, allowing designers to interactively sketch out 3D concepts in real time using a virtual workspace. A database of a number of hand signs was built for both architectural hand signs (preliminary study) and Product Design hand signs. A marker set model with a total of eight markers (five on the left hand and three on right hand/marker pen) was designed and used in the capture of hand gestures with the use of an Optical Motion Capture System. A preliminary testing session was successfully completed to determine whether the Motion Capture system would be suitable for a real-time application, by effectively modelling a train station in an offline state using hand motion and gesture. An OpenGL software application was programmed using C++ and the Microsoft Foundation Classes which was used to communicate and pass information of captured motion from the EVaRT system to the user

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 großen Variationen und unterschiedlichen Stilen ist für Simulationsanwendungen wie die Spieleindustrie, psychologische Experimente und ergonomische Analysen von wachsendem Interesse. Datengetriebene Bewegungssyntheseansätze sind leistungsstarke Werkzeuge für die Erstellung realitätsgetreuer Charakteranimationen. Mit der Entwicklung von Motion-Capture-Technologien sind nun immer mehr Motion-Daten öffentlich verfügbar. Die effiziente Wiederverwendung einer großen Menge von Motion-Daten zur Erstellung neuer Bewegungen für beliebige Szenarien stellt jedoch eine Herausforderung dar, insbesondere für die unüberwachte Bewegungssynthesemethoden. Das Lernen der Verteilung von Motion-Daten kann eine kompakte Repräsentation 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 für verschiedene Szenarien zu erstellen. Die Arbeit in dieser Dissertation wird in drei Hauptkapiteln vorgestellt. Wir untersuchen zunächst, wie Variation in Bewegungsdaten dargestellt wird. Das Erlernen eines kompakten latenten Raums, der Bewegungsvariationen ausdrucksvoll enthalten kann, ist für die Modellierung von Bewegungsdaten unerlässlich. Wir schlagen einen neuartigen Ansatz zum Lernen des latenten Bewegungsraums vor, der die räumlich-zeitlichen Eigenschaften von Bewegungsdaten intrinsisch angehen kann. Zweitens stellen wir unser Morphable Graph Framework für die menschliche Bewegungsmodellierung und -synthese für Montage-Workshop- Szenarien vor. Es wurde eine Reihe von Studien durchgeführt, um statistische Bewegungsmodellierungs und syntheseansätze für komplexe Anwendungsfälle in Montagewerkstätten anzuwenden. Schließlich zeigen wir anhand einer begrenzten Anzahl von Beispielen, wie die Stilvariationen menschlicher Aktivitäten modelliertwerden können. Natürliche menschliche Bewegungen weisen ein reiches Repertoire an Stilen und Persönlichkeiten auf. Es ist jedoch schwierig, genügend Beispiele für datengetriebene Ansätze 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 natürlich aussehenden Variationen menschlicher Bewegungen mit einem ähnlichen Stil wie das Ziel erzeugen kann

Pose-Timeline for Propagating Motion Edits

Motion editing often requires repetitive operations for modifying similar action units to give a similar effector impression. This paper proposes a system for efficiently and flexibly editing the sequence of iterative actionsby a few intuitive operations. Our system visualizes a motion sequence on a summary timeline with editablepose-icons, and drag-and-drop operations on the timeline enable intuitive controls of temporal properties of themotion such as timing, duration, and coordination. This graphical interface is also suited to transfer kinematicaland temporal features between two motions through simple interactions with a quick preview of the resultingposes. Our method also integrates the concept of edit propagation by which the manual modification of one actionunit is automatically transferred to the other units that are robustly detected by similarity search technique. Wedemonstrate the efficiency of our pose-timeline interface with a propagation mechanism for the timing adjustmentof mutual actions and for motion synchronization with a music sequence