Academic Profile
Associate Professor
Department of Computer Science, University of Cyprus,
Senior Research Fellow at CYENS Centre of Excellence
Research interests on character animation (analysis, classification, and synthesis), involving motion capture, machine learning and generative AI for virtual humans, digital heritage (intangible cultural creations), and VR/AR/XR environments.
Short professional biography
Andreas Aristidou is an Associate Professor at the Department of Computer Science, University of Cyprus, member of the Graphics & Extended Reality Lab, and a Senior Research Fellow at the CYENS Centre of Excellence with special interest in computer graphics and character animation. He completed his PhD as a Cambridge European Trust fellow at the Department of Information Engineering, University of Cambridge and holds an MSc in Mobile and Personal Communications from King's College London, where he graduated with honors. Dr. Aristidou also obtained a BSc in Informatics and Telecommunications from the National and Kapodistrian University of Athens. He previously worked as a research fellow at Shandong University (China), Reichman University (Israel), and the University of Cyprus, and participated in a number of EU funded projects. He is currently on the editorial board of The Visual Computer (TVC) and Heritage journals, and previosuly served a guest editor-in-chief for Advances in Applied Clifford Algebras (AACA) journal. He is a senior member of the Association of Computing Machinery (ACM, SM from 2020), the Institute of Electrical and Electronic Engineers (IEEE, SM from 2019), and Eurographics. He is also a member of the Cyprus Scientific and Technical Chamber, where he previously served as a member of the Research Committee, while from 2020-2021, he was a member of the Cyprus Parallel Parliament for research, innovation, and digital governance. He has received numerous fellowships, distinctions, and research grants from highly competitive local, EU, and international agencies.
Areas of interest & current projects
His main research interests lie at the intersection of computer graphics, virtual reality, and vision. He specializes in character animation (analysis, classification, and synthesis) and motion capture, where he uses techniques like machine learning and generative AI models to create virtual humans. His work also extends to digital heritage (intangible creations), virtual performances, VR/AR/MR environments, and the application of Conformal Geometric Algebra in computer graphics. He participated in a number of EU funded projects, and he collaborates with EU creative industries to design innovative algorithms for motion synthesis and character retargeting.
HAMLET: Human-centred generative Ai fraMework for culturaL industriEs’ digital Transition
HAMLET aims to democratize access to Generative AI for Europe's Cultural and Creative Industries (CCIs), enabling entities of all sizes to benefit from AI for digital transition. It integrates a collaborative platform and AI tools to foster shared investments, collaboration and innovation, while promoting sustainable and inclusive frameworks for creative industries.
EMBRACE: Empowering Skills for Butterfly Monitoring to Enable Resilience to Climate Change
EMBRACE aims to improve butterfly monitoring, contributing to EU environmental and biodiversity goals, particularly regarding climate change resilience. It engages farmers, agronomists, environmental scientists, and other stakeholders through education, including e-learning, a 3D digital butterfly museum, and replicable training materials. By involving a wide range of groups, from civil servants to citizens, the project fosters awareness and participation in biodiversity conservation efforts across Europe.
PREMIERE: Performing arts in a new era: AI and XR tools for better understanding, preservation, enjoyment and accessibility
PREMIERE modernizes the performing arts, focusing on dance and theatre, by using advanced digital technologies to support the whole lifecycle of performances. It develops an AI, XR and 3D–powered ecosystem that serves diverse communities involved in performing arts productions.
CLIPE: Creating Lively Interactive Populated Environments
The primary objective of CLIPE is to train a generation of innovators and researchers in the field of virtual characters simulation and animation. Advances in technology are pushing towards making VR/AR worlds a daily experience. Whilst virtual characters are an important component of these worlds, bringing them to life and giving them interaction and communication abilities requires highly specialized programming combined with artistic skills, and considerable investments: millions spent on countless coders and designers to develop video-games is a typical example. The research objective of CLIPE is to design the next-generation of VR-ready characters.
DEMONSTRATION: DEep MOtioN SynThesis foR character AnimaTION
DEMONSTRATION covers a wide range of multidisciplinary topics that are in line with the recent tendencies in computer graphics, character animation, and virtual reality. It aims at investigating modern trends in machine (deep, convolutional, adversarial, and reinforcement) learning, with ultimate target to provide ingenious solutions for overcoming the current limitations in character animation, and essentials for future improvements in a wide range of ambitious and challenging projects.
ALADDIN: Advancing the motion capture technoLogy viA DistributeD wIreless Networks
Motion capture is a technology used for turning the observations of a moving subject into 3D position and orientation information, stimulating our ability to define and virtually portray complex movements. The ALADDIN project aims at the development of such a technology that goes beyond conventional methods, is cost-effective, and minimizes the risks associated with capturing in dynamic situations. The proposed system is easily scalable and intrinsic, in the sense that measurements are not taken by external devices, enabling efficient capturing in outdoor environments, with state-of-the-art acquisition accuracy.
SCHEDAR: Safeguarding the Cultural HEritage of Dance through Augmented Reality
SCHEDAR aims to contribute in the safeguarding of our Intangible Cultural Heritage (ICH), and more specifically folk dancing, by providing novel solutions to the three key challenges of archiving, reusing & repurposing, and ultimately disseminating ICH creations. A comprehensive set of new guidelines will be devised, and a framework and software tools for leveraging existing ICH motion databases. Data acquisition will be undertaken holistically; encompassing data related to the performance, the performer, the kind of the dance, the hidden/untold story, etc. Innovative use of state-of-the-art multisensory Augmented Reality technology will be used to enable direct interaction with the dance, providing new experiences and training in traditional dance.
ReTrack: Advancing site level management through innovative reptiles' tracking and behavioural decryption
ReTrack aims to tackle the problem of animal monitoring and habitat utilization, specifically designed for small-sized reptiles. It will advance the scientific knowledge in the fields of (a) reptile locomotion, (b) behavioral analysis, and (c) conservation, through the development of innovative monitoring techniques and approaches. Utilizing state-of-the-art technology in remote sensing, advanced photogrammetry and image pattern recognition, ReTrack aims to create fine-scale micro-habitat utilization maps for two common Cypriot species, a lizard (Stellagama stellio) and a snake (Dolichophis jugularis), thus advancing site level management through the designing of more targeted, species based management and conservation actions.
ITN-DCH: Initial Training Network for Digital Cultural Heritage
ITN-DCH aims to analyze, design, research, develop and validate an innovative multi-disciplinary and inter-sectorial research training framework that covers the entire lifecycle of digital CH research for a cost–effective preservation, documentation, protection and presentation of cultural heritage. CH is an integral element of Europe and vital for the creation of a common European identity and one of the greatest assets for steering Europe’s social, economic development and job creation. However, the current research training activities in CH are fragmented and mostly design to be of a single-discipline, failing to cover the whole lifecycle of Digital Cultural Heritage (DCH) research, which is by nature a multi-disciplinary and inter-sectorial research agenda.
ViDaPe: Visual Dance Performance for Interactive Characters
ViDaPe aims at generating a virtual animated character that interacts, in real-time, with a real dancing performer to compose a contemporary dancing show. The proposed research will explore innovative topics with special interest in the area of computer animation, including methods which smoothly combine optical motion capture (mocap) data with kinematic techniques, human figure modelling, a novel methodology for motion classification and partial-body motion synthesis. The system will be adjusted dynamically according to the performers' actions and responses, offering the maximum possible interaction between the natural and virtual performer.
SIM.POL.VR: Virtual Reality Police Simulator
SIM.POL.VR focuses in investigating the usability and applicability of Virtual Reality in training special forces. It proposes the development of a platform using Computer Graphics techniques (as oppossed to the existing video-based system that is restrictive), for the creation of a 3D Visualisation Tool enriched with tools suitable for the formation and customisation of dynamic scenarios.
Selected publications and full list by type
A complete list of publications is also available on Google Scholar.
DRUMS: Drummer Reconstruction Using Midi Sequences
DRUMS is a MIDI-driven system that generates expressive, full-body drumming performances, combining precise rhythmic accuracy with realistic hand, stick, upper-body, and facial movements. By integrating BiLSTM-based hand motion prediction, phrase-matched upper-body and facial expressions, and procedural foot control through a modular IK framework, our method produces visually convincing and musically aligned drummer animations for applications in digital performance.
Disaster evacuation of the old city of Nicosia
This paper presents a digital twin–based simulation of evacuation scenarios in Nicosia’s historic walled Old Town, showing how dense urban morphology, gate blockages, and infrastructure changes affect evacuation efficiency and highlighting the need for coordinated, adaptive flow-management strategies to reduce disaster risks in historic cities.
MPACT: Mesoscopic Profiling and Abstraction of Crowd Trajectories
MPACT is a framework that transforms unlabelled crowd data into controllable simulation parameters using image-based encoding and a parameter prediction network trained on synthetic image–profile pairs. It enables intuitive crowd authoring and behavior analysis, achieving high scores in believability, plausibility, and behavioral fidelity across evaluations and user studies.
Interactive Media for Cultural Heritage
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.
DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization
DragPoser is a deep-learning-based motion reconstruction system that uses variable sparse sensors as input, achieving real-time high end-effector position accuracy through a pose optimization process within a structured latent space. Incorporating a Temporal Predictor network with a Transformer architecture, DragPoser surpasses traditional methods in precision, producing natural and temporally coherent poses, and demonstrating robustness and adaptability to dynamic constraints and various input configurations.
CEDRL: Simulating Diverse Crowds with Example-Driven Deep Reinforcement Learning
This paper introduces CEDRL (Crowds using Example-driven Deep Reinforcement Learning), a framework that models diverse and adaptive crowd behaviors by leveraging multiple datasets and a reward function aligned with real-world observations. The approach enables real-time controllability and generalization across scenarios, showcasing enhanced behavior complexity and adaptability in virtual environments.
DOI · Pre-print Paper · Video · Code · Supplementary Materials · Project · BibTeX
Multi-Modal Instrument Performance: A musical database
This paper introduces the Multi-Modal Instrument Performances (MMIP) database, the first dataset to combine synchronized high-quality 3D motion capture, audio, video, and MIDI data for musical performances involving guitar, piano, and drums. It highlights the challenges of capturing and managing such multimodal data, offering an open-access repository with tools for exploration, visualization, and playback.
A novel multidisciplinary approach for reptile movement and behavior analysis
This paper introduces a multidisciplinary approach to studying reptile behavior, combining tri-axial accelerometers, video recordings, motion capture systems, and 3D reconstruction to create detailed digital archives of movements and behaviors. Using two Mediterranean reptiles as case studies, it highlights the potential of this method to advance research on complex and understudied behaviors, offering ecological insights and tools for behavioral analysis.
Deep convolutional generative adversarial networks in retinitis pigmentosa disease images augmentation and detection
This study utilizes Deep Convolutional Generative Adversarial Networks (DCGAN) and hybrid VGG16-XGBoost techniques to enhance medical datasets, focusing on retinitis pigmentosa, a rare eye condition. The proposed method improves image clarity, dataset augmentation, and detection accuracy, achieving over 90% in key performance metrics and a 19% increase in baseline classification accuracy.
Identifying and Animating Movement of Zeibekiko Sequences by Spatial Temporal Graph Convolutional Network with Multi Attention Modules
This study employs optical motion capture technology to document and translate the Zeibekiko dance into a 3D virtual environment. Using a Spatial Temporal Graph Convolutional Network with Multi Attention Modules (ST-GCN-MAM), the system accurately captures and classifies essential dance sequences by focusing on key body regions, enabling precise, realistic virtual animations with applications in gaming, video production, and digital heritage preservation.
Underwater Virtual Exploration of the Ancient Port of Amathus
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.
Design and Implementation of an Interactive Virtual Library based on its Physical Counterpart
This work explores the creation of a digital replica of a physical Library, using photogrammetry and 3D modelling. A Virtual Reality (VR) platform was developed to immerse users in a virtual library experience, which can also serve as a community and knowledge hub. A perceptual study was conducted to understand the current usage of physical libraries, examine the users’ experience in VR, and identify the requirements and expectations in the development of a virtual library counterpart. Five key usage scenarios were implemented, as a proof-of-concept, with emphasis on 24/7 access, functionality, and interactivity. A user evaluation study endorsed all its key attributes and future viability.
Virtual Instrument Performances (VIP): A Comprehensive Review
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.
Digitizing Traditional Dances Under Extreme Clothing: The Case Study of Eyo
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 · Project · BibTeX
SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
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.
Collaborative VR: Solving riddles in the concept of escape rooms
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.
Dancing in virtual reality as an inclusive platform for social and physical fitness activities: A survey
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 · PDF · Survey Questionnaire · BibTeX
Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking
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.
Collaborative Museum Heist with Reinforcement Learning
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.
Let's All Dance: Enhancing Amateur Dance Motions
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 · PDF · Video · Code · Data · Supplementary Materials · Project · BibTeX
Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
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 · Pre-print Paper · Video · Dance Motion DB · Project · BibTeX
Pose Representations for Deep Skeletal Animation
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.
DOI· Pre-print Paper· Video· Code· Project· Geometric Algebra· BibTeX
Digitizing Wildlife: The case of reptiles 3D virtual museum
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.
Safeguarding our Dance Cultural Heritage
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.
Virtual Dance Museums: the case of Greek/Cypriot folk dancing
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.
Adult2Child: Motion Style Transfer using CycleGANs
This paper presents an effective style translation method that transfers 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.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
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.
Salsa dance learning evaluation and motion analysis in gamified virtual reality environment
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.
CGA, a mathematical framework for motion continuity in deep neural networks
Digital Dance Ethnography: Organizing Large Dance Collections
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.
Real-time 3D Human Pose and Motion Reconstruction from Monocular RGB Videos
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.
Deep Motifs and Motion Signatures
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.
Self-similarity Analysis for Motion Capture Cleaning
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.
Inverse Kinematics Techniques in Computer Graphics: A Survey
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.
Style-based Motion Analysis for Dance Composition
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.
Emotion Control of Unstructured Dance Movements
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.
Hand Tracking with Physiological Constraints
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.
Continuous body emotion recognition system during theater performances
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.
Extending FABRIK with Model Constraints
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.
Folk Dance Evaluation Using Laban Movement Analysis
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.
Emotion analysis and classification: Understanding the performers' emotions using LMA entities
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.
A Conformal Geometric Algebra framework for Mixed Reality and mobile display
Cypriot Intangible Cultural Heritage: Digitizing Folk Dances
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.
Emotion Recognition for Exergames using Laban Movement Analysis
Exergames currently lack the ability to detect and adapt to players' emotional states to enhance the user experience. We propose using body motion features based on Laban Movement Analysis (LMA) to classify four emotional states with high accuracy, as demonstrated by experimental results.
Real-time marker prediction and CoR estimation in optical motion capture
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.
FABRIK: a fast, iterative solver for the inverse kinematics problem
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.
Inverse Kinematics solutions using Conformal Geometric Algebra
DOI · Chapter · PPT · Geometric Algebra · BibTeX
Motion capture, cultural heritage, and open resources
I curate and maintain five publicly accessible motion-capture and 3D-model repositories, spanning music performance, natural heritage, biodiversity, and dance preservation. These collections provide high-quality research-ready datasets (including motion-capture recordings, 3D models, HD video, audio, and metadata) supporting interdisciplinary work in culture, education, conservation, and digital-humanities research.
Virtual Butterfly Exhibition
A digital collection featuring photorealistic 3D models of native butterfly species, accompanied by taxonomy, host plant data, phenology, and conservation information. Created to support ecological education, scientific study, and biodiversity awareness among the public.
Multi-Modal Instrument Performances
An open-access dataset combining synchronized high-resolution 3D motion capture, MIDI streams, HD video, and studio-quality audio from professional musicians on piano, guitar, and drums. Designed for research on musical expressivity, gesture, skill acquisition, and performance analysis.
Cyprus 3D reptiles
A repository of animated, high-fidelity 3D models of reptile species native to Cyprus, capturing both biological morphology and live motion for reference, conservation, and educational use. The platform bridges tangible and intangible heritage by documenting color, structure, posture, movement, and behavioral signalling.
Virtual Dance Museum
A dynamic 3D archive presenting traditional Cypriot and Greek dances through fully animated characters dressed in authentic attire, accompanied by metadata, photographs, and cultural context. Viewers can explore each performance interactively, experiencing dance traditions as they appear in real festivals.
Dance Motion Capture Database
A publicly accessible digital archive preserving traditional dance through state-of-the-art motion-capture recordings of expert performers. The repository provides high-quality datasets (including FBX, C3D, BVH, SMPL formats, and videos with music), and continues to expand as new performances are documented over time.
Courses, supervision & dissertation topics
The course teaches intermediate and advanced programming concepts, techniques and tools through a language that compiles to machine code. It familiarizes students with advanced constructs for handling memory and files, and covers compilation, debugging, documentation, and optimization of software. Emphasis is placed on methodological aspects of developing large-scale system software that addresses complex problems. Basic UNIX commands for programmers are also introduced.
The course teaches the basic principles of computer graphics. Students become familiar with scene construction, scene hierarchies, camera specification, projections of primitives, clipping, visible surface determination, polygon rasterisation (z-buffer), texture mapping, local and global illumination, shadows, ray tracing, radiosity, and real-time acceleration techniques. Both theoretical foundations and practical skills are provided through industry-standard tools such as OpenGL and the Unity game engine.
This course introduces core machine learning algorithms and their use in computer vision and computer graphics. It also functions as a graduate-level seminar with weekly readings, short summaries, and discussions of recent research papers.
Computer Graphics
Audio-Visual Foundations
Computer Networks
Digital Logic Design
Information Technology
Information Engineering Laboratories
Interactive 3D Applications for Education and Cultural Heritage
Design and development of immersive environments such as educational games, virtual museums, and exploratory learning tools. Focus on user interaction, gamification, and visual storytelling through real-time graphics.
Evaluating Motion Quality Through Real + Synthetic Data Fusion
Systematic study of motion generation quality by blending motion captured data with AI-generated animations. Determine ideal proportions where motion realism begins to degrade and visualize quality metrics.
Motion-Driven Sonification and Neural Music Generation
Explore how human motion can be translated into sound and music in real time or offline. Possible directions include feature extraction from motion data, generative music models, and interactive audio-visual feedback systems.
Laban Movement Analysis for Motion Stylization and Transfer
Use LMA descriptors to analyze movement expressiveness and employ them as intermediates in animation generation. The goal is to manipulate style, emotion, or intent during retargeting or generative modeling.
Mapping Laban Movement Scores into Arousal–Valence Emotional Space
Investigate how LMA parameters relate to emotional perception and model their projection onto the Arousal/Valence diagram. May include dataset collection, statistical correlation, and visualization or generative movement-emotion mapping.
Supervision & prospective students
![Portrait of [Student Name]](students/Theodoros_Kyriakou.jpg)
I am currently welcoming motivated PhD / MSc / Research Associate / internship candidates who are eager to work in a fast-paced, creative research environment. Strong programming skills are essential, while experience in computer graphics, machine learning, or VR/AR is a valuable bonus. If you are passionate, curious, and excited to build innovative digital technologies, I would love to hear from you.
Editorial & community roles
Funded projects and selected recognitions
Get in touch
