PDE-based Facial Animation: Making the Complex Simple

YesDirect parameterisation is among the most widely used facial animation techniques but requires complicated ways to animate face models which have complex topology. This paper develops a simple solution by introducing a PDE-based facial animation scheme. Using a PDE face model means we only need to animate a group of boundary curves without using any other conventional surface interpolation algorithms. We describe the basis of the method and show results from a practical implementation.EPSR

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

Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties

Model-based approaches to 3D hand tracking have been shown to perform well in a wide range of scenarios. However, they require initialisation and cannot recover easily from tracking failures that occur due to fast hand motions. Data-driven approaches, on the other hand, can quickly deliver a solution, but the results often suffer from lower accuracy or missing anatomical validity compared to those obtained from model-based approaches. In this work we propose a hybrid approach for hand pose estimation from a single depth image. First, a learned regressor is employed to deliver multiple initial hypotheses for the 3D position of each hand joint. Subsequently, the kinematic parameters of a 3D hand model are found by deliberately exploiting the inherent uncertainty of the inferred joint proposals. This way, the method provides anatomically valid and accurate solutions without requiring manual initialisation or suffering from track losses. Quantitative results on several standard datasets demonstrate that the proposed method outperforms state-of-the-art representatives of the model-based, data-driven and hybrid paradigms.Comment: BMVC 2015 (oral); see also http://lrs.icg.tugraz.at/research/hybridhape

Neural Volumetric Blendshapes: Computationally Efficient Physics-Based Facial Blendshapes

Computationally weak systems and demanding graphical applications are still mostly dependent on linear blendshapes for facial animations. The accompanying artifacts such as self-intersections, loss of volume, or missing soft tissue elasticity can be avoided by using physics-based animation models. However, these are cumbersome to implement and require immense computational effort. We propose neural volumetric blendshapes, an approach that combines the advantages of physics-based simulations with realtime runtimes even on consumer-grade CPUs. To this end, we present a neural network that efficiently approximates the involved volumetric simulations and generalizes across human identities as well as facial expressions. Our approach can be used on top of any linear blendshape system and, hence, can be deployed straightforwardly. Furthermore, it only requires a single neutral face mesh as input in the minimal setting. Along with the design of the network, we introduce a pipeline for the challenging creation of anatomically and physically plausible training data. Part of the pipeline is a novel hybrid regressor that densely positions a skull within a skin surface while avoiding intersections. The fidelity of all parts of the data generation pipeline as well as the accuracy and efficiency of the network are evaluated in this work. Upon publication, the trained models and associated code will be released

The eroticism of artificial flesh in Villiers de L'Isle Adam's L'Eve Future

Villiers de L'Isle Adam's 'L'Eve Future' published in 1886 features a fictional version of the inventor Thomas Edison who constructs a complex, custom-made android for Englishman Lord Ewald as a substitute for his unsatisfactory lover. Hadaly, the android, has a number of literary and cultural precursors and successors. Her most commonly accepted ancestor is Olympia in E. T. A. Hoffmann's 'The Sandman' (1816) and among her fascinating descendants are Oskar Kokoschka's 'Silent Woman'; Model Borghild, a sex doll designed by German technicians during World War II;‘Caracas' in Tommaso Landolfi's short story ‘Gogol's Wife' (1954); a variety of gynoids and golems from the realms of science fiction, including Ira Levin's 'Stepford Wives' (1972); and, most recently, that silicon masterpiece - the Real Doll. All, arguably, have their genesis in the classical myth of Pygmalion. This essay considers the tension between animation and stasis in relation to this myth, and explores the necrophiliac aesthetic implicit in Villiers's novel

Electromyogram (EMG) Driven System Based Virtual Reality for Prosthetic and Rehabilitation Devices

The users of current prosthetic and rehabilitation devices are facing problems to adapt to their new hosts or not receiving any bio-feedback despite rehabilitation process and retraining, particularly when working with Electromyogram (EMG) signals. In characterizing virtual human limbs, as a potential prosthetic device in 3D virtual reality, patients are able to familiarize themselves with their new appendage and its capabilities in a virtual training environment or can see their movements intention. This paper presents a Virtual Reality (VR) based design and implementation of a below-shoulder 3D human arm capable of 10-class EMG based motions driven system of biomedical EMG signal. The method considers a signal classification output as potential control stimulus to drive the virtual prosthetic prototype. A hierarchical design methodology is adopted based on anatomical structure, congruent with Virtual Reality Modeling Language (VRML) architecture. The resulting simulation is based on a portable, self-contained VR model implementation paired with an instrumental virtual control-select board capable of actuating any combinations of singular or paired kinematic 10-class EMG motions. The built model allows for multiple degree of freedom profiles as the classes can be activated independently or in conjunction with others allowing enhanced arm movement