Savvas Zotos, Marilena Lemonari, Michael Konstantinou, Anastasios Yiannakidis, Georgios Pappas, Panayiotis Kyriakou, Ioannis N. Vogiatzakis, Andreas Aristidou
IEEE Computer Graphics and Applications, Early Access, May 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.
Qiu Zhou, Manyi Li, Qiong Zeng, Andreas Aristidou, Xiaojing Zhang, Lin Chen, Changhe Tu
Computational Visual Media, Early Access, May 2022.
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.
Andreas Aristidou, Anastasios Yiannakidis, Kfir Aberman, Daniel Cohen-Or, Ariel Shamir, Yiorgos Chrysanthou
IEEE Transactions on Visualization and Computer Graphics, Early Access, March 2022.
To be 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.
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.
Nefeli Andreou, Andreas Lazarou, Andreas Aristidou, Yiorgos Chrysanthou
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Andreas Aristidou, Joan Lasenby
Guide to Geometric Algebra in Practice, L. Dorst and J. Lasenby (Eds), pages 47-62, Springer Verlag, 2011.
An iterative Inverse Kinematics solver is implemented using Conformal Geometric Algebra. We use a human hand as an example of implementation where a constrained version of the IK solver is employed for pose tracking. The hand is modelled using CGA, taking advantage of CGA’s compact and geometrically intuitive framework, that basic entities in CGA, such as spheres, lines, planes and circles, are simply represented by algebraic objects.
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.
© 2017 Andreas Aristidou