Publications
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.
DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization
Jose Luis Pontón, Eduard Pujol, Andreas Aristidou, Carlos Andújar, Nuria Pelechano
arXiv.org > cs.GR > arXiv:2406.14567, Apr 2024
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.
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.
LexiCrowd: A Learning Paradigm towards Text to Behaviour Parameters for Crowds
Marilena Lemonari, Nefeli Andreou, Nuria Pelechano, Panayiotis Charalambous, Yiorgos Chrysanthou
Presented at: The Creating Lively Interactive Populated Environments (CLIPE 2024) Workshop, The Eurographics Association, Apr 2024
This work uses a pre-trained Large Language Model (LLM) to generate pseudo-pairs of text and behavior labels, then trains a variational auto-encoder (VAE) on this synthetic dataset, constraining the latent space with a latent label loss for interpretable behavior parameters. Our model, tested with human-provided textual descriptions of crowd datasets, can parameterize unseen sentences to produce novel behaviors compatible with simulator parameters, demonstrating its potential for text-to-crowd generation and full sentence generation from behavior profiles.
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, Apr 2024
Presented at: Eurographics 2024, EG'24 STAR papers
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
Temi Ami-Williams, Christina-Georgia Serghides, Andreas Aristidou
Journal of Cultural Heritage, Volume 67, pages 145–157, February 2024., Mar 2024
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.
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., Jan 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.
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., Oct 2023
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.
Collaborative VR: Solving riddles in the concept of escape rooms
Afxentis Ioannou, Marilena Lemonari, Fotis Liarokapis, Andreas Aristidou
Presented at: International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET, Oct 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.
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., Sep 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.
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, Volume 40, pages 4055–4070, 2024., Aug 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.
Collaborative Museum Heist with Reinforcement Learning
Eleni Evripidou, Andreas Aristidou, Panayiotis Charalambous
Computer Animation and Virtual Worlds, Volume 34, Issue 3-4, May 2023., May 2023
Presented at: 36th International Conference on Computer Animation and Social Agents, CASA'23
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.