Publications

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

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

Presented at: Proceedings of the EUROGRAPHICS Workshop on Graphics and Cultural Heritage, GCH'21, Nov 2021

This paper presentes 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.

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Background segmentation in multicolored illumination environments

Nikolas Ladas, Paris Kaimakis, Yiorgos Chrysanthou

The Visual Computer, Oct 2020

We present an algorithm for the segmentation of images into background and foreground regions. The proposed algorithm utilizes a physically based formulation of scene appearance which explicitly models the formation of shadows originating from color light sources. This formulation enables a probabilistic model to distinguish between shadows and foreground objects in challenging images.

DOI

Adult2Child: Motion Style Transfer using CycleGANs

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

Presented at: ACM SIGGRAPH Conference on Motion, Interaction, and Games, MIG'20, Oct 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 database bibtex project page

Using Epistemic Game Development to Teach Software Development Skills

Christos Gatzoulis, Andreas S. Andreou, Panagiotis Zaharias, Yiorgos Chrysanthou

International Journal of Game-Based Learning (IJGBL), 10(4), Oct 2020

DOI

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, Sep 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 more robust 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, Sep 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, Nov 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 difference between semantically similar motions. It illustrates a number of different organization trees, and portray the chronological and geographical evolution of dances.

DOI paper video database bibtex project page

Why did the human cross the Road?

Panayiotis Charalambous, Yiorgos Chrysanthou

Presented at: ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG ’19), Article 47, 1–2. Best Poster Award, Oct 2019

Humans at rest tend to stay at rest. Humans in motion tend to cross the road – Isaac Newton.” Even though this response is meant to be a joke to indicate the answer is quite obvious, this important feature of real world crowds is rarely considered in simulations. Answering this question involves several things such as how agents balance between reaching goals, avoid collisions with heterogeneous entities and how the environment is being modeled. As part of a preliminary study, we introduce a reinforcement learning framework to train pedestrians to cross streets with bidirectional traffic. Our initial results indicate that by using a very simple goal centric representation of agent state and a simple reward function, we can simulate interesting behaviors such as pedestrians crossing the road through crossings or waiting for cars to pass.

DOI paper video database

Adult2Child Age Regression Using CycleGANs

Thomas Domas, Yuzhu Dong, Brendan John, Ariel Shamir, Andreas Aristidou, Eakta Jain

Presented at: ACM Symposium on Applied Perception (SAP'19), Barcelona, Spain, Sep 2019

paper bibtex

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), May 2019

Presented at: 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

Automatic Environment Map Registration

Ulysse Larvy, Celine Loscos, Yiorgos Chrysanthou

Presented at: Eurographics Short Papers, Jan 2019

DOI paper

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, Dec 2018

Presented at: 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 database 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, Dec 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 database bibtex project page

How Responsiveness, Group Membership and Gender Affect the Feeling of Presence in Immersive Virtual Environments Populated With Virtual Crowds

Marios Kyriakou and Yiorgos Chrysanthou

Presented at: ACM SIGGRAPH Conference on Motion, Interaction, and Games, MIG'18, Limassol, Cyprus, Nov 2018

DOI

Inverse Kinematics Techniques in Computer Graphics: A Survey

Andreas Aristidou, Joan Lasenby, Yiorgos Chrysanthou, Ariel Shamir

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