Publications
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, Jul 2025
This work employs Spatial Temporal Graph Convolutional Networks (ST-GCN) to recognize and annotate folk dance movements, using the Greek Zeibekiko dance as a case study to address the challenges of motion segmentation in complex, expressive dances. By enabling accurate classification and annotation of motion-captured dance data, the method supports cultural heritage preservation, dance analysis, and educational applications.
Multi - Modal Signal Processing for Avatar Motion Adaptation
Amir Azizi, Panayiotis Charalambous, Yiorgos Chrysanthou
Presented at: The 25th International Conference on Digital Signal Processing (DSP), Jun 2025
We propose a multi-modal framework for adapting avatar motion using an encoder–decoder architecture with phase-based features, enabling control over attributes like speed and intensity. By integrating inputs such as text, speech, video, and webcam data, the approach allows flexible and intuitive motion adjustment, demonstrating efficient and scalable performance across multiple datasets.
DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization
Jose Luis Pontón, Eduard Pujol, Andreas Aristidou, Carlos Andújar, Nuria Pelechano
Computer Graphics Forum, Volume 44, Issue 2., Apr 2025
Presented at: Eurographics 2025, EG'25 proceedings
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
Andreas Panayiotou, Andreas Aristidou, Panayiotis Charalambous
Computer Graphics Forum, Volume 44, Issue 2., Apr 2025
Presented at: Eurographics 2025, EG'25 proceedings
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.
Multi-Modal Instrument Performance: A musical database
Theodoros Kyriakou, Andreas Aristidou, Panayiotis Charalambous
Computer Graphics Forum, Volume 44, Issue 2., Apr 2025
Presented at: Eurographics 2025, EG'25 proceedings
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.
Lead: Latent realignment for human motion diffusion
Nefeli Andreou, Xi Wang, Victoria Fernández Abrevaya, Marie‐Paule Cani, Yiorgos Chrysanthou, Vicky Kalogeiton
Computer Graphics Forum, e70093, Apr 2025
This work proposes LEAD, a method that combines latent diffusion with a realignment mechanism to create a semantically structured space for generating realistic human motion from natural language. It enables both high-quality motion synthesis and motion textual inversion, outperforming modern methods in realism, diversity, and alignment with textual input.
DeepSafe: Two-level deep learning approach for disaster victims detection
Amir Azizi, Panayiotis Charalambous, Yiorgos Chrysanthou
Virtual Reality & Intelligent Hardware, 7(2), Pages 139-154, Apr 2025
We present DeepSafe, a two-level deep learning framework for disaster victim detection that combines YOLOv8 for classification and Detectron2 for precise localization. The approach improves accuracy and speed in identifying victims under challenging conditions, offering an effective tool for real-time search and rescue operations.
Demonstrating the effectiveness of combining heuristic and data-driven methods to achieve scalable and adaptive motion styles
Amir Azizi, Panayiotis Charalambous, Yiorgos Chrysanthou
Presented at: 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Mar 2025
We propose a hybrid motion style transfer framework that combines heuristic methods (e.g., RBF and phase features) with data-driven autoencoders to balance efficiency and adaptability. The approach reduces dataset requirements while maintaining high-quality results, demonstrating scalable and flexible motion generation across multiple datasets.
Deep convolutional generative adversarial networks in retinitis pigmentosa disease images augmentation and detection
Paweł Powroźnik, Maria Skublewska-Paszkowska, Katarzyna Nowomiejska, Andreas Aristidou, Andreas Panayides, Robert Rejdak
Advances in Science and Technology Research Journal, Volume 19, no. 2, pages 321-340., Nov 2024
This study leverages 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
Maria Skublewska-Paszkowska, Paweł Powroźnik, Marcin Barszcz, Krzysztof Dziedzic, Andreas Aristidou
Advances in Science and Technology Research Journal, Volume 18, no. 8, pages 217-227., Nov 2024
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
Andreas Alexandrou, Filip Skola, Dimitrios Skarlatos, Stella Demesticha, Fotis Liarokapis, Andreas Aristidou
Journal of Cultural Heritage, Volume 70, pages 181-193, November–December 2024., Sep 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.
Design and Implementation of an Interactive Virtual Library based on its Physical Counterpart
Christina-Georgia Serghides, Giorgos Christoforidis, Nikolas Iakovides, Andreas Aristidou
Virtual Reality, Volume 28, Article Number 124, June, 2024, Springer., Jun 2024
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.
Improving Image Reconstruction using Incremental PCA-Embedded Convolutional Variational Auto-Encoder
Amir Azizi, Panayiotis Charalambous, Yiorgos Chrysanthou
Presented at: The 32nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2024), May 2024
We propose an efficient image reconstruction method using Convolutional Variational Autoencoders enhanced with Incremental PCA to better capture key latent features. The approach improves image quality and scalability while reducing processing time, demonstrating strong performance on MNIST.
Behavioral Landmarks: Inferring Interactions from Data
Marilena Lemonari, Panayiotis Charalambous, Andreas Panayiotou, Yiorgos Chrysanthou, Julien Pettré
Presented at: Eurographics 2024 Posters, May 2024
Weaim to unravel complex agent-environment interactions from trajectories, by explaining agent paths as combinations of predefined basic behaviors. We detect trajectory points signifying environment-driven behavior changes, ultimately disentangling interactions in space and time; our framework can be used for environment synthesis and authoring, shown by our case studies.
Overcoming Challenges of Cycling Motion Capturing and Building a Comprehensive Dataset
Panayiotis Kyriakou, Marios Kyriakou, Yiorgos Chrysanthou
Presented at: The Creating Lively Interactive Populated Environments (CLIPE 2024) Workshop, The Eurographics Association, Apr 2024
This article outlines a methodology for capturing cyclist motion using motion capture (mocap) hardware and creating a publicly available comprehensive dataset. It features a modular system with innovative marker placement, and the resulting dataset is used to produce 3D visualizations and various data representations, which are shared in an online library for public access and collaborative research.