Publications

Virtual Environment Navigation Assisted by Neural Networks

Georgios Kyrlitsias, Amyr Borges Fortes Neto, Panayiotis Charalambous, Marios Avraamides, Yiorgos Chrysanthou

Presented at: Virtual Humans and Crowds for Immersive Environments (VHCIE '18), May 2018

Applications using Virtual Environments (VE) are becoming increasingly popular due to greater computational capacity and improvements in graphics processing units and tracking devices. As a result, much research has been carried out on various aspects of VEs, including the input devices that can be used to navigate scenes when physical movement is not permitted. Here, we test whether implementing a neural network to assist users avoid collisions with virtual obstacles, can benefit the navigation experience. Our hypothesis was that users with no gaming experience in particular, would appreciate the assistance of the neural network in navigation. However, our pilot data suggest the exact opposite: participants with video game experience liked the assisted navigation more than participants with no video game experience.

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Hand Tracking with Physiological Constraints

Andreas Aristidou

The Visual Computer, 34(2): 213-228, Jan 2018

We present a simple and efficient methodology for tracking and reconstructing 3D hand poses. Using an optical motion capture system, where markers are positioned at strategic points, we manage to acquire the movement of the hand and establish its orientation using a minimum number of markers. An Inverse Kinematics solver was then employed to control the postures of the hand, subject to physiological constraints that restrict the allowed movements to a feasible and natural set.

DOI paper video bibtex project page

Interaction with virtual crowd in Immersive and semi‐Immersive Virtual Reality systems

Marios Kyriakou, Xueni Pan, Yiorgos Chrysanthou

Comp. Animation & Virtual Worlds, 28(5): e1729, Sep 2017

DOI

Emotion Control of Unstructured Dance Movements

Andreas Aristidou, Qiong Zeng, Efstathios Stavrakis, KangKang Yin, Daniel Cohen-Or, Yiorgos Chrysanthou, Baoquan Chen

Presented at: ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, SCA'17. Eurographics Association, Jul 2017

We present a motion stylization technique suitable for highly expressive mocap data, such as contemporary dances. The method varies the emotion expressed in a motion by modifying its underlying geometric features. Even non-expert users can stylize dance motions by supplying an emotion modification as the single parameter of our algorithm.

DOI paper video code bibtex project page

Improving Tracking Accuracy Using Illumination Neutralization and High-Dynamic Range Imaging

Nikolas Ladas, Yiorgos Chrysanthou, Celine Loscos

High Dynamic Range Video Concepts, Technologies and Applications, ed. by Alan Chalmers, Patrizio Campisi, Peter Shirley, and Igor Olaizola. 1st edition. Academic Press. pp. 203–213, Jun 2017

DOI

Probabilistic Background Modelling for Sports Video Segmentation

Nikolas Ladas, Paris Kaimakis, Yiorgos Chrysanthou

Presented at: 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pp 517-525, Porto, Portugal, Jan 2017

DOI

Continuous body emotion recognition system during theater performances

Simon Senecal, Louis Cuel, Andreas Aristidou, Nadia Magnenat-Thalmann

Comp. Animation & Virtual Worlds, 27(3-4): 311-320, May 2016

Presented at: Computer Animation and Social Agents - CASA'16

We propose a system for continuous emotional behavior recognition expressed by people during communication based on their gesture and their whole body dynamical motion. The features used to classify the motion are inspired by the Laban Movement Analysis. Using a trained neural network and annotated data, our system is able to describe the motion behavior as trajectories on the Russell Circumplex Model diagram during theater performances over time.

DOI paper bibtex

Extending FABRIK with Model Constraints

Andreas Aristidou, Yiorgos Chrysanthou, Joan Lasenby

Comp. Animation & Virtual Worlds, 27(1): 35-57, Jan 2016

This paper addresses the problem of manipulating articulated figures in an interactive and intuitive fashion for the design and control of their posture using the FABRIK algorithm; the algorithm has been extended to support a variation of different joints and has been evaluated on a humanoid model.

DOI paper video bibtex project page

Data Driven Crowd Evaluation

Panayiotis Charalambous, Yiorgos Chrysanthou

Simulating Heterogeneous Crowds with Interactive Behaviors. Ed. by Nuria Pelechano, Jan M. Allbeck, Mubbasir Kapadia, and Badler Norman I. CRC Press. Chap. 10, pp. 217–237, Jan 2016

DOI

Learning Heterogeneous Crowd Behavior from the Real World

Panayiotis Charalambous, Yiorgos Chrysanthou

Simulating Heterogeneous Crowds with Interactive Behaviors. Ed. by Nuria Pelechano, Jan M. Allbeck, Mubbasir Kapadia, and Badler Norman I. CRC Press. Chap. 3, pp. 53–73, Jan 2016

DOI

Folk Dance Evaluation Using Laban Movement Analysis

Andreas Aristidou, Efstathios Stavrakis, Panayiotis Charalambous, Yiorgos Chrysanthou, Stephania L. Himona

ACM Journal on Computing and Cultural Heritage, 8(4): 1-19, Aug 2015

Presented at: Best paper award at EG GCH 2014.

We present a framework based on the principles of Laban Movement Analysis (LMA) that aims to identify style qualities in dance motions, and can be subsequently used for motion comparison and evaluation. We have designed and implemented a prototype virtual reality simulator for teaching folk dances in which users can preview dance segments performed by a 3D avatar and repeat them. The user’s movements are captured and compared to the folk dance template motions; then, intuitive feedback is provided to the user based on the LMA components.

DOI paper video bibtex project page

Emotion analysis and classification: Understanding the performers’ emotions using LMA entities

Andreas Aristidou, Panayiotis Charalambous, Yiorgos Chrysanthou

Computer Graphics Forum, 34(6): 262–276, Apr 2015

Presented at: Eurographics 2016

We proposed a variety of features that encode characteristics of motion, in terms of Laban Movement Analysis, for motion classification and indexing purposes. Our framework can be used to extract both the body and stylistic characteristics, taking into consideration not only the geometry of the pose but also the qualitative characteristics of the motion. This work provides some insights on how people express emotional states using their body, while the proposed features can be used as alternative or complement to the standard similarity, motion classification and synthesising methods.

DOI paper video database bibtex project page

Interaction with virtual agents – Comparison of the participants’ experience between an IVR and a semi-IVR system

Marios Kyriakou, Xueni Pan, Yiorgos Chrysanthou

Presented at: IEEE Virtual Reality (VR) conference. IEEE. pp. 217–218, Mar 2015

DOI

Toward Energy-Aware Balancing of Mobile Graphics

Efstathios Stavrakis, Marios Polychronis, Nectarios Pelekanos, Alessandro Artusi, Panayiotis Hadjichristodoulou, Yiorgos Chrysanthou

Presented at: Mobile Devices and Multimedia: Enabling Technologies, Algorithms and Applications, SPIE 9411, Mar 2015

DOI

LMA-Based Motion Retrieval for Folk Dance Cultural Heritage

Andreas Aristidou, Efstathios Stavrakis, Yiorgos Chrysanthou

LNCS, volume 8740, pages 207-216, Nov 2014

Presented at: 5th International Conference on Cultural Heritage (EuroMed'14), Limassol, Cyprus

DOI paper bibtex