Editorial Board

The Visual Computer

Associated Editor, 2018 - Today


Editor, 2022 - Today

Advances in Applied Clifford Algebras

Guest Editor-in-Chief, 2021-2022



Underwater Virtual Exploration of the Ancient Port of Amathus

Andreas Alexandrou, Filip Skola, Dimitrios Skarlatos, Stella Demesticha, Fotis Liarokapis, Andreas Aristidou

Journal of Cultural Heritage, Submitted on April 2024.

This work focuses on the digital reconstruction and visualization of underwater cultural heritage, providing a gamified virtual reality (VR) experience of Cyprus' ancient Amathus harbor. Utilizing photogrammetry, our immersive VR environment enables seamless exploration and interaction with this historic site. Advanced features such as guided tours, procedural generation, and machine learning enhance realism and user engagement. User studies validate the quality of our VR experiences, highlighting minimal discomfort and demonstrating promising potential for advancing underwater exploration and conservation efforts.

[DOI] [paper] [video] [bibtex] [project page]

P2C: A Paths-to-Crowds Framework to Parameterize Behaviors

Marilena Lemonari, Andreas Panayiotou, Nuria Pelechano, Theodoros Kyriakou, Yiorgos Chrysanthou, Andreas Aristidou, Panayiotis Charalambous

IEEE Transactions on Visualization and Computer Graphics, Submitted, January 2024.

We present P2C, a method for simulating realistic and diverse crowds by parameterizing reference crowd data. This approach enables fine-grained control, allowing artists to modify intuitive behavior parameters in localized regions during runtime, facilitating the creation of customizable agent behaviors and interactions in crowd simulations. The method integrates four fundamental behaviors into an existing Reinforcement Learning-based system and demonstrates its potential through predictive power evaluation on real data, comparing favorably against existing baselines.

[DOI] [pre-print paper] [video] [bibtex] [project page]

Virtual Instrument Performances (VIP): A Comprehensive Review

Theodoros Kyriakou, Mercè Álvarez, Andreas Panayiotou, Yiorgos Chrysanthou, Panayiotis Charalambous, Andreas Aristidou

Computer Graphics Forum, Volume 43, Issue 2, April 2024.

Presented at Eurographics 2024, EG'24 STAR papers, April, 2024.

The evolving landscape of performing arts, driven by advancements in Extended Reality (XR) and the Metaverse, presents transformative opportunities for digitizing musical experiences. This comprehensive survey explores the relatively unexplored field of Virtual Instrument Performances (VIP), addressing challenges related to motion capture precision, multi-modal interactions, and the integration of sensory modalities, with a focus on fostering inclusivity, creativity, and live performances in diverse settings.

[DOI] [paper] [bibtex] [project page]

Digitizing Traditional Dances Under Extreme Clothing: The Case Study of Eyo

Temi Ami-Williams, Christina-Georgia Serghides, Andreas Aristidou

Journal of Cultural Heritage, Volume 67, pages 145–157, February 2024.

The video has been presented at the International Council for Traditional Music (ICTM) 2023 and the Cyprus Dance Film Festival (CDFF) 2023.

This work examines the challenges of capturing movements in traditional African masquerade garments, specifically the Eyo masquerade dance from Lagos, Nigeria. By employing a combination of motion capture technologies, the study addresses the limitations posed by "extreme clothing" and offers valuable insights into preserving cultural heritage dances. The findings lead to an efficient pipeline for digitizing and visualizing folk dances with intricate costumes, culminating in a visually captivating animation showcasing an Eyo masquerade dance performance.

[DOI] [pre-print paper] [video] [promo video] [video:behind the scenes] [bibtex] [project page]

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

Jose Luis Pontón, Haoran Yun, Andreas Aristidou, Carlos Andújar, Nuria Pelechano

ACM Transactions on Graphics, Volume 43, Issue 1, Article No.: 5, pages 1–14.

Presented at SIGGRAPH Asia 2023.

SparsePoser is a novel deep learning-based approach that reconstructs full-body poses using only six tracking devices. The system uses a convolutional autoencoder to generate high-quality human poses learned from motion capture data and a lightweight feed-forward neural network IK component to adjust hands and feet based on the corresponding trackers.

[DOI] [paper] [code] [video] [bibtex] [project page]

Collaborative VR: Solving riddles in the concept of escape rooms

Afxentis Ioannou, Marilena Lemonari, Fotis Liarokapis, Andreas Aristidou

International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2023.

This work explores alternative means of communication in collaborative virtual environments (CVEs) and their impact on users' engagement and performance. Through a case study of a collaborative VR escape room, we conduct a user study to evaluate the effects of nontraditional communication methods in computer-supported cooperative work (CSCW). Despite the absence of traditional interactions, our study reveals that users can effectively convey messages and complete tasks, akin to real-life scenarios.

[DOI] [paper] [bibtex]

Dancing in virtual reality as an inclusive platform for social and physical fitness activities: A survey

Bhuvaneswari Sarupuri, Richard Kulpa, Andreas Aristidou, Franck Multon

The Visual Computer, Accepted on June 2023.

This paper qualitatively evaluates 292 users of a VR dancing platform, exploring their motivations, experiences, and requirements. We employ OpenAI's Artificial Intelligence platform for automatic extraction of response categories. The focus is on VR as an inclusive platform for social and physical dancing activities.

[DOI] [paper] [bibtex] [Survey Questionnaire]

Motion-R^3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking

Jubo Yu, Tianxiang Ren, Shihui Guo, Fengyi Fang, Kai Wang, Zijiao Zeng, Yazhan Zhang, Andreas Aristidou, Yipeng Qin

arXiv.org > cs > arXiv:2304.01672

In this work we present a new method for motion annotation based on the representativeness of motion data in a given dataset. Our ranks motion data based on their representativeness in a learned motion representation space. The paper also introduces a dual-level motion contrastive learning method to learn the motion representation space in a more informative way. The proposed method is efficient and can adapt to frequent requirements changes, enabling agile development of motion annotation models.

[DOI] [paper] [video] [bibtex] [project page]

Collaborative Museum Heist with Reinforcement Learning

Eleni Evripidou, Andreas Aristidou, Panayiotis Charalambous

Computer Animation and Virtual Worlds, Volume 34, Issue 3-4, May 2023.

Presented at the 36th International Conference on Computer Animation and Social Agents, CASA'23, May, 2023.

In this paper, we present our initial findings of applying Reinforcement Learning techniques to a museum heist game, where trained robbers with different skills learn to cooperate and maximize individual and team rewards while avoiding detection by scripted security guards and cameras, showcasing the feasibility of training both sides concurrently in an adversarial game setting.

[DOI] [paper] [video] [code] [bibtex] [project page]

Let's All Dance: Enhancing Amateur Dance Motions

Qiu Zhou, Manyi Li, Qiong Zeng, Andreas Aristidou, Xiaojing Zhang, Lin Chen, Changhe Tu

Computational Visual Media, Vol.9, No.3, September 2023

In this paper, we present a deep model that enhances professionalism to amateur dance movements, allowing the movement quality to be improved in both the spatial and temporal domains. We illustrate the effectiveness of our method on real amateur and artificially generated dance movements. We also demonstrate that our method can synchronize 3D dance motions with any reference audio under non-uniform and irregular misalignment.

[DOI] [paper] [supplementary materials] [video] [code] [data] [bibtex] [project page]

Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure

Andreas Aristidou, Anastasios Yiannakidis, Kfir Aberman, Daniel Cohen-Or, Ariel Shamir, Yiorgos Chrysanthou

IEEE Transactions on Visualization and Computer Graphics, Volume 29, Issue 8, August 2023.

Presented at ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, SCA'22, September, 2022.

In this work, we present a music-driven neural framework that generates realistic human motions, which are rich, avoid repetitions, and jointly form a global structure that respects the culture of a specific dance genre. We illustrate examples of various dance genre, where we demonstrate choreography control and editing in a number of applications.

[DOI] [paper] [video] [Dance Motion Database] [bibtex] [project page]

Pose Representations for Deep Skeletal Animation

Nefeli Andreou, Andreas Aristidou, Yiorgos Chrysanthou

Computer Graphics Forum, Volume 41, Issue 8, December 2022.

Presented at ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, SCA'22, September, 2022.

In this work we present an efficient method for training neural networks, specifically designed for character animation. We use dual quaternions as the mathematical framework, and we take advantage of the skeletal hierarchy, to avoid rotation discontinuities, a common problem when using Euler angle or exponential map parameterizations, or motion ambiguities, a common problem when using positional data. Our method does not requires re-projection onto skeleton constraints to avoid bone stretching violation and invalid configurations, while the network is propagated learning using both rotational and positional information.

[DOI] [paper] [video] [code] [bibtex] [project page]

Virtual Library in the concept of digital twins

Nikolas Iakovides, Andreas Lazarou, Panayiotis Kyriakou, Andreas Aristidou

International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2022.

In this work, we reconstruct the Limassol Municipal University Library in the concept of a digital twin. To do so, we conducted a perceptual survey to understand the current use of physical libraries, examine the user’s experience with VR, and identify potential use cases of VR libraries. Based on the outcome, we design five use case scenarios where we demonstrate the potential use of virtual libraries.

[DOI] [paper] [bibtex] [3D model] [project page]

Digitizing Wildlife: The case of reptiles 3D virtual museum

Savvas Zotos, Marilena Lemonari, Michael Konstantinou, Anastasios Yiannakidis, Georgios Pappas, Panayiotis Kyriakou, Ioannis N. Vogiatzakis, Andreas Aristidou

IEEE Computer Graphics and Applications, Feature Article, Volume 42, Issue 5, Sept/Oct. 2022.

In this paper, we design and develop a 3D virtual museum with holistic metadata documentation and a variety of captured reptile behaviors and movements. Our main contribution lies on the procedure of rigging, capturing, and animating reptiles, as well as the development of a number of novel educational applications.

[DOI] [paper] [video] [bibtex] [project page]

Safeguarding our Dance Cultural Heritage

Andreas Aristidou, Alan Chalmers, Yiorgos Chrysanthou, Celine Loscos, Franck Multon, Joseph E. Parkins, Bhuvan Sarupuri, Efstathios Stavrakis

Eurographics Tutorials, April 26, 2022.

In this tutorial, we show how the European Project, SCHEDAR, exploited emerging technologies to digitize, analyze, and holistically document our intangible heritage creations, that is a critical necessity for the preservation and the continuity of our identity as Europeans.

[DOI] [paper] [teaser video] [bibtex] [project page]

Virtual Dance Museums: the case of Greek/Cypriot folk dancing

Andreas Aristidou, Nefeli Andreou, Loukas Charalambous, Anastasios Yiannakidis, Yiorgos Chrysanthou

EUROGRAPHICS Workshop on Graphics and Cultural Heritage, GCH'21, Bournemouth, United Kingdom, November 2021.

This paper presents a virtual dance museum that has been developed to allow for widely educating the public, most specifically the youngest generations, about the story, costumes, music, and history of our dances. The museum is publicly accessible, and also enables motion data reusability, facilitating dance learning applications through gamification.

[DOI] [paper] [bibtex] [project page]

Adult2Child: Motion Style Transfer using CycleGANs

Yuzhu Dong, Andreas Aristidou, Ariel Shamir, Moshe Mahler, Eakta Jain

ACM SIGGRAPH Conference on Motion, Interaction, and Games, MIG'20, October 2020.

This paper presents an effective style translation method that tranfers adult motion capture data to the style of child motion using CycleGANs. Our method allows training on unpaired data using a relatively small number of sequences of child and adult motions that are not required to be temporally aligned. We have also captured high quality adult2child 3D motion capture data that are publicly available for future studies.

[DOI] [paper] [video] [code] [Adult2Child Motion Database] [bibtex] [project page]

MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency

Mingyi Shi, Kfir Aberman, Andreas Aristidou, Taku Komura, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen

ACM Transaction on Graphics, 40(1), Article 1, 2020.

Presented at SIGGRAPH Asia 2020.

MotioNet is a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. It decomposes sequences of 2D joint positions into two separate attributes: a single, symmetric, skeleton, encoded by bone lengths, and a sequence of 3D joint rotations associated with global root positions and foot contact labels. We show that enforcing a single consistent skeleton along with temporally coherent joint rotations constrains the solution space, leading to a morerobust handling of self-occlusions and depth ambiguities.

[DOI] [paper] [video] [code] [bibtex] [project page]

Salsa dance learning evaluation and motion analysis in gamified virtual reality environment

Simon Senecal, Niels A. Nijdam, Andreas Aristidou, Nadia Magnenat-Thalmann

Multimedia Tools and Applications, 79 (33-34): 24621-24643, September 2020.

We propose an interactive learning application in the form of a virtual reality game, that aims to help users to improve their salsa dancing skills. The application consists of three components, a virtual partner with interactive control to dance with, visual and haptic feedback, and a game mechanic with dance tasks. Learning is evaluated and analyzed using Musical Motion Features and the Laban Motion Analysis system, prior and after training, showing convergence of the profile of non-dancer toward the profile of regular dancers, which validates the learning process.

[DOI] [paper] [video] [bibtex] [project page]

Digital Dance Ethnography: Organizing Large Dance Collections

Andreas Aristidou, Ariel Shamir, Yiorgos Chrysanthou

ACM Journal on Computing and Cultural Heritage, 12(4), Article 29, 2019.

This paper presents a method for contextually motion analysis that organizes dance data semantically, to form the first digital dance ethnography. The method is capable of exploiting the contextual correlation between dances, and distinguishing fine-grained differences between semantically similar motions. It illustrates a number of different organization trees, and portrays the chronological and geographical evolution of dances.

[DOI] [paper] [video] [Dance Motion Database] [bibtex] [project page]

Real-time 3D Human Pose and Motion Reconstruction from Monocular RGB Videos

Anastasios Yiannakides, Andreas Aristidou, Yiorgos Chrysanthou

Comp. Animation & Virtual Worlds, 30(3-4), 2019.

Proceedings of Computer Animation and Social Agents - CASA'19

In this paper, we present a method that reconstructs articulated human motion, taken from a monocular RGB camera. Our method fits 2D deep estimated poses of multiple characters, with the 2D multi-view joint projections of 3D motion data, to retrieve the 3D body pose of the tracked character. By taking into consideration the temporal consistency of motion, it generates natural and smooth animations, in real-time, without bone length violations.

[DOI] [paper] [video] [bibtex] [project page]

Deep Motifs and Motion Signatures

Andreas Aristidou, Daniel Cohen-Or, Jessica K. Hodgins, Yiorgos Chrysanthou, Ariel Shamir

ACM Transaction on Graphics, 37(6), Article 187, 2018.

Proceedings of SIGGRAPH Asia 2018.

We introduce deep motion signatures, which are time-scale and temporal-order invariant, offering a succinct and descriptive representation of motion sequences. We divide motion sequences to short-term movements, and then characterize them based on the distribution of those movements. Motion signatures allow segmenting, retrieving, and synthesizing contextually similar motions.

[DOI] [paper] [video] [Dance Motion Database] [bibtex] [project page]

Self-similarity Analysis for Motion Capture Cleaning

Andreas Aristidou, Daniel Cohen-Or, Jessica K. Hodgins, Ariel Shamir

Computer Graphics Forum, 37(2): 297-309, 2018.

Proceedings of Eurographics 2018.

Our method automatically analyzes mocap sequences of closely interacting performers based on self-similarity. We define motion-words consisting of short-sequences of joints transformations, and use a time-scale invariant similarity measure that is outlier-tolerant to find the KNN. This allows detecting abnormalities and suggesting corrections.

[DOI] [paper] [video] [bibtex] [project page]

Inverse Kinematics Techniques in Computer Graphics: A Survey

Andreas Aristidou, Joan Lasenby, Yiorgos Chrysanthou, Ariel Shamir

Computer Graphics Forum, 37(6): 35-58, 2018.

Presented at Eurographics 2018 (STAR paper).

In this survey, we present a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view. The most popular IK methods are discussed with regard to their performance, computational cost and the smoothness of their resulting postures, while we suggest which IK family of solvers is best suited for particular problems. Finally, we indicate the limitations of the current IK methodologies and propose future research directions.

[DOI] [paper] [bibtex] [project page]

Style-based Motion Analysis for Dance Composition

Andreas Aristidou, Efstathios Stavrakis, Margarita Papaefthimiou, George Papagiannakis, Yiorgos Chrysanthou

The Visual Computer, 34(12), 1725-1737, 2018.

This work presents a motion analysis and synthesis framework, based on Laban Movement Analysis, that respects stylistic variations and thus is suitable for dance motion synthesis. Implemented in the context of Motion Graphs, it is used to eliminate potentially problematic transitions and synthesize style-coherent animation, without requiring prior labeling of the data.

[DOI] [paper] [video] [Dance Motion Database] [bibtex] [project page]

Emotion Control of Unstructured Dance Movements

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

ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, SCA'17. Eurographics Association, July, 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] [Dance Motion Database] [bibtex] [project page]

Hand Tracking with Physiological Constraints

Andreas Aristidou

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

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, 2016.

Proceedings of 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, 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]

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, 2015.

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, 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] [Dance Motion Database] [bibtex] [project page]

Cypriot Intangible Cultural Heritage: Digitizing Folk Dances

Andreas Aristidou, Efstathios Stavrakis, Yiorgos Chrysanthou

Cyprus Computer Society journal, Issue 25, pages 42-49, 2014.

We aim to preserve the Cypriot folk dance heritage, creating a state-of-the-art publicly accessible digital archive of folk dances. Our dance library, apart from the rare video materials that are commonly used to document dance performances, utilises three dimensional motion capture technologies to record and archive high quality motion data of expert dancers.

[paper] [bibtex] [project page]

Real-time marker prediction and CoR estimation in optical motion capture

Andreas Aristidou, Joan Lasenby

The Visual Computer, 29 (1): 7-26, 2013.

An integrated framework is presented which predicts the occluded marker positions using a Variable Turn Model within an Unscented Kalman filter. Inferred information from neighbouring markers is used as observation states; these constraints are efficient, simple, and real-time implementable. An Inverse Kinematics solver is then applied ensuring that the bone lengths remain constant over time; the system can thereby maintain a continuous data-flow.

[DOI] [paper] [video] [bibtex] [project page]

FABRIK: a fast, iterative solver for the inverse kinematics problem

Andreas Aristidou, Joan Lasenby

Graphical Models, 73(5): 243-260, 2011

A novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is described that avoids the use of rotational angles or matrices, and instead finds each joint position via locating a point on a line. Thus, it converges in few iterations, has low computational cost and produces visually realistic poses. Constraints can easily be incorporated within FABRIK and multiple chains with multiple end effectors are also supported.

[DOI] [paper] [video] [bibtex] [project page]

Other Publications


Interactive Media for Cultural Heritage

Editors: Fotis Liarokapis, Maria Shehade, Andreas Aristidou, Yiorgos Chrysanthou

Springer Series on Cultural Computing, Springer Cham, 2024.

This edited book explores the latest advancements in interactive media applied to Digital Cultural Heritage research, covering areas from visual data acquisition to immersive experiences like extended reality and digital storytelling. Structured into four sections, it offers theoretical discussions and diverse case studies, making it a valuable resource for academics, scholars, researchers, and students interested in interdisciplinary approaches to cultural heritage preservation and exploration through emerging technologies.

[DOI] [book] [bibtex]

Book Chapters

  • Motion labelling and recognition: A case study on the Zeibekiko dance
    Maria Skublewska-Paszkowska, Pawel Powroznik, Marilena Lemonari, Andreas Aristidou
    Interactive Media for Cultural Heritage, In F. Liarokapis, M. Shahade, A. Aristidou and Y. Chrysanthou (Eds), Springer Series in Cultural Computing, Springer, 2024
    [DOI] [paper] [bibtex]
  • Inverse Kinematics solutions using Conformal Geometric Algebra
    Andreas Aristidou, Joan Lasenby
    Guide to Geometric Algebra in Practice, In L. Dorst and J. Lasenby (Eds), pages 47-62, Springer Verlag, 2011.
    [DOI] [paper] [ppt] [bibtex] [project page]

Conference Proceedings

  • LMA-Based Motion Retrieval for Folk Dance Cultural Heritage
    Andreas Aristidou, Efstathios Stavrakis, Yiorgos Chrysanthou
    In Proceedings of the 5th International Conference on Cultural Heritage (EuroMed'14), LNCS, volume 8740, pages 207-216, Limassol, Cyprus, November 3 – 8, 2014.
    [DOI] [paper] [bibtex]
  • Motion Analysis for Folk Dance Evaluation [Best Paper Award]
    Andreas Aristidou, Efstathios Stavrakis, Yiorgos Chrysanthou
    In Proceedings of the 12th EUROGRAPHICS Workshop on Graphics and Cultural Heritage (GCH'14), pages 55-64, Darmstadt, Germany, October 5 – 8, 2014.
    [DOI] [paper] [bibtex]
  • Feature extraction for human motion indexing of acted dance performances
    Andreas Aristidou, Yiorgos Chrysanthou
    In Proceedings of the 9th International Conference on Computer Graphics Theory and Applications (GRAPP'14), pages 277-287, Portugal, January 05-08, 2014.
    [DOI] [paper] [bibtex]
  • Motion indexing of different emotional states using LMA components
    Andreas Aristidou, Yiorgos Chrysanthou
    In SIGGRAPH Asia Technical Briefs (SA’13), ACM, New York, USA, 21:1-21:4, 2013.
    [DOI] [paper] [bibtex]
  • Emotion Recognition for Exergames using Laban Movement Analysis
    Haris Zacharatos, Christos Gatzoulis, Yiorgos Chrysanthou, Andreas Aristidou
    In Proceedings of ACM Motion in Games (MIG'13), Ireland, November 7-9, 2013.
    [DOI] [paper] [bibtex]
  • Digitization of Cypriot Folk Dances
    Efstathios Stavrakis, Andreas Aristidou, Maria Savva, Stephania Loizidou Himona, Yiorgos Chrysanthou
    In Proceedings of the 4th International Conference on Progress in Cultural Heritage Preservation (EuroMed'12), LNCS, Volume 7616, pages 404-413, 2012.
    [DOI] [paper] [bibtex]
  • Motion Capture with Constrained Inverse Kinematics for Real-Time Hand Tracking
    Andreas Aristidou, Joan Lasenby
    In IEEE Proceedings of the 4th International Symposium on Communications, Control and Signal Processing (ISCCSP'10), Limassol, Cyprus, May 3-5, 2010.
    [DOI] [paper] [bibtex]
  • Predicting Missing Markers to Drive Real-Time Centre of Rotation Estimation
    Andreas Aristidou, Jonathan Cameron, Joan Lasenby
    In Proceedings of the International Conference on Articulated Motion and Deformable Objects (AMDO'08), LNCS, Vol. 5098, pages 238-247, Mallorca, Spain, July 9-11, 2008.
    [DOI] [paper] [bibtex]
  • Real-Time Estimation of Missing Markers in Human Motion Capture
    Andreas Aristidou, Jonathan Cameron, Joan Lasenby
    In IEEE Proceedings of the International Conference on Bioinformatics and Biomedical Engineering (iCBBE'08), pages 1343-1346, Shanghai, China, May 16-18, 2008.
    [DOI] [paper] [bibtex]
  • Tracking Multiple Sports Players for Mobile Display
    Andreas Aristidou, Paul Pangalos, Hamid Aghvami
    In Proceeding of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'07), pages 53-59, Las Vegas, USA, June 25-28, 2007.
    [DOI] [paper] [bibtex]
  • Violent Content Classification using Audio Features
    Theodoros Giannakopoulos, Dimitrios Kosmopoulos, Andreas Aristidou, Sergios Theodoridis
    In Proceedings of the Hellenic Artificial Intelligence Conference SETN-06, LNCS, Volume 3955, pages 502-507, Heraklion, Crete, Greece, May 18-20, 2006.
    [DOI] [paper] [bibtex]

Short Papers and Posters

  • CGA, a mathematical framework for motion continuity in deep neural networks
    Andreas Aristidou
    In Proceedings of the International Conference on Empowering Novel Geometric Algebra for Graphics & Engineering (ENGAGE'20), Geneva, Switzerland, September 2020.
    [paper] [bibtex]
  • Virtual Dance Museum: the case of Cypiot folk dancing
    Andreas Aristidou, Anastasios Yiannakidis, Yiorgos Chrysanthou
    In Proceedings of the International Conference on Emerging Technologies and the Digital Transformation of Museums and Heritage Sites, RISE IMET, Nicosia, Cyprus, June 2020.
    [paper] [bibtex]
  • Adult2Child Age Regression Using CycleGANs
    Thomas Domas, Yuzhu Dong, Brendan John, Ariel Shamir, Andreas Aristidou, Eakta Jain
    In Proceedings of the ACM Symposium on Applied Perception (SAP'19), Barcelona, Spain, September 19-20, 2019.
    [paper] [bibtex]
  • A Conformal Geometric Algebra framework for Mixed Reality and mobile display
    Margarita Papaefthimiou, George Papagiannakis, Andreas Aristidou, Marinos Ioannides
    In Proceedings of the 6th Conference on Applied Geometric Algebra in Computer Science and Engineering (AGACSE'15), Barcelona, Spain, July 2015.
    [paper] [bibtex]

Final Thesis

  • Tracking and Modelling Motion for Biomechanical Analysis
    Andreas Aristidou
    A dissertation submitted to the University of Cambridge for the Degree of Doctor of Philosophy, Cambridge, October 2010.
    Supervisor: Dr Joan Lasenby, Examiners: Prof William J. Fitzgerald & Prof Adrian Hilton.
    [DOI] [paper] [bibtex]
  • Reliable indexing and recognition of violent scenes using audio features
    Andreas Aristidou
    Annual journal of the Department of Informatics & Telecommunications, Best dissertations of the year, pages 127-136, Athens, Greece, 2006.
    Supervisor: Prof Sergios Theodoridis
    [paper] [bibtex]

Technical Reports

  • Marker Prediction and Skeletal Reconstruction in Motion Capture Technology
    Andreas Aristidou, Yiorgos Chrysanthou
    Technical Report (UCY-CS-TR-13-2), University of Cyprus, August 2013.
    [paper] [bibtex]
  • Inverse Kinematics: a review of existing techniques and introduction of a new fast iterative solver
    Andreas Aristidou, Joan Lasenby
    Technical Report (CUEDF-INFENG, TR-632), University of Cambridge, September 2009
    [paper] [bibtex]
  • Methods for Real-time Restoration and Estimation in Optical Motion Capture
    Andreas Aristidou, Joan Lasenby, Jonathan Cameron
    Technical Report (CUEDF-INFENG, TR-619), University of Cambridge, January 2009.
    [paper] [bibtex]

© 2017 Andreas Aristidou