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.
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.
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.
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.
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.
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.
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
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.
Self-similarity Analysis for Motion Capture Cleaning
Andreas Aristidou, Daniel Cohen-Or, Jessica K. Hodgins, Ariel Shamir
Computer Graphics Forum, 37(2): 297-309, May 2018
Presented at: 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.
Virtual Environment Navigation Assisted by Neural Networks
Georgios Kyrlitsias, Amyr Borges Fortes Neto, Panayiotis Charalambous, Marios Avraamides, Yiorgos Chrysanthou
Presented at: Virtual Humans and Crowds for Immersive Environments (VHCIE '18), May 2018
Applications using Virtual Environments (VE) are becoming increasingly popular due to greater computational capacity and improvements in graphics processing units and tracking devices. As a result, much research has been carried out on various aspects of VEs, including the input devices that can be used to navigate scenes when physical movement is not permitted. Here, we test whether implementing a neural network to assist users avoid collisions with virtual obstacles, can benefit the navigation experience. Our hypothesis was that users with no gaming experience in particular, would appreciate the assistance of the neural network in navigation. However, our pilot data suggest the exact opposite: participants with video game experience liked the assisted navigation more than participants with no video game experience.
Hand Tracking with Physiological Constraints
The Visual Computer, 34(2): 213-228, Jan 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.
Emotion Control of Unstructured Dance Movements
Andreas Aristidou, Qiong Zeng, Efstathios Stavrakis, KangKang Yin, Daniel Cohen-Or, Yiorgos Chrysanthou, Baoquan Chen
Presented at: ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, SCA'17. Eurographics Association, Jul 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.