On Recursive Edit Distance Kernels with Application to Time Series Classification

This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) kernels that are positive definite if some sufficient conditions are satisfied. The sufficient conditions we end-up with are original and weaker than those proposed in earlier works, although a recursive regularizing term is required to get the proof of the positive definiteness as a direct consequence of the Haussler's convolution theorem. The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to conclude that, when the pairwise distance matrix obtained from the training data is \textit{far} from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for the classical elastic distances we have tested.Comment: 14 page

Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures

In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.Comment: ICPR 2014, International Conference on Pattern Recognition, Stockholm : Sweden (2014

Codage, représentation et traitement du geste instrumental. Application à la synthèse de sons musicaux par simulation de mécanismes instrumentaux

This work, mainly concerned with the conception and the elaboration of an informatic tool for musical creation deals with instrumental gesture in order to control in real time sound-synthesis processes, and to analyse its relationship with musical composition. This work is in two parts: 1/ a preliminary analysis of instrumental gesture typology leads to propose a coding which takes into account a spatial ant temporal organization of gestural information. We used these principles to conceive and realize an informatic system, which enables the coding and the pre-processing of gestural signals in real time. 2/ The study then focuses on searching a structural representation of gesture, which brings out some expressive features of gesture. A method which consists of identifying the gestural human action on a physical object with a simple but time-varying mechanical model, is proposed. This approach leads to a homogeneous description of both gesture and instrument which takes into account the coherency of the man-instrument interaction and can be used as a new way to manipulate structural characteristics of gesture.Dans le cadre de la conception et de l’élaboration d’un outil informatique pour la création musicale, on s’intéresse au geste instrumental, pour contrôler en temps réel des processus de synthèse sonore par simulation de mécanismes instrumentaux, et pour étudier sa relation à la composition musicale. Deux aspects de cette étude sont abordés : 1/ une analyse préliminaire sur la typologie du geste instrumental conduit à proposer un codage du geste qui traduit une organisation spatiale et temporelle des données gestuelles captées. Ces fondements nous servent de support pour concevoir et réaliser un système matériel et logiciel, permettant la capture, le codage et le prétraitement en temps réel des signaux gestuels. 2/ L’étude est ensuite focalisée vers la recherche d’un espace de représentation structurelle du geste, qui traduit une certaine forme d’expressivité de l’action gestuelle. Nous proposons une méthode qui consiste à identifier l’action gestuelle humaine sur un objet physique à un modèle mécanique simple mais évolutif. Ce principe conduit à une description homogène du geste de l’instrument qui tient compte de la cohérence de l’interaction homme-objet manipulé, et peut constituer un point d’entrée à une approche structurelle de composition par gestes

Enhancing the Visualization of Percussion Gestures by Virtual Character Animation

International audienceA new interface for visualizing and analyzing percussion gestures is presented, proposing enhancements of existing motion capture analysis tools. This is achieved by offering a percussion gesture analysis protocol using motion capture. A virtual character dynamic model is then designed in order to take advantage of gesture characteristics, yielding to improve gesture analysis with visualization and interaction cues of different types

Captured Motion Data Processing for Real Time Synthesis of Sign Language

International audienceThe work described in this abstract presents a roadmap towards the creation and specification of a virtual humanoid capable of performing expressive gestures in real time. We present a gesture motion data acquisition protocol capable of handling the main articulators involved in human expressive gesture (whole body, fingers and face). We then present the postprocessing of captured data leading to a motion database complying with our motion specification language and capable of feeding data driven animation techniques

Animation faciale basée données : un état de l'art

National audienceCet article dresse un panorama des différentes problématiques liées à l'animation faciale basée données et présente les dernières avancées et solutions proposées par l'état de l'art. Le but de l'animation basée données est d'animer des personnages virtuels reproduisant les actions effectuées par des acteurs humains. Dans ce contexte, le visage joue un rôle prépondérant puisqu'il est l'un des principaux vecteurs de l'émotion et de la communication chez l'humain. Par ailleurs, contrairement au reste du corps dont les mouvements sont contraints par des articulations et des os, les déformations du visage suivent une autre forme de dynamique ce qui en fait un cas d'étude à part. Les applications sont diverses, par exemple, la création d'avatars virtuels ou l'animation de personnages présentant un comportement naturel. Dans ce papier, nous aborderons la question depuis la capture des données faciales (les différents dispositifs et méthodes de capture) jusqu'aux méthodes de synthèse exploitant ces données

CVM-Net: Motion Reconstruction from a Single RGB Camera with a Fully Supervised DCNN

National audienceMany solutions have been proposed for 3D human pose estimation from video data. However, only a few of them take into account temporal features. In this article, we present a method focusing on this temporal aspect and show promising results. Our approach consists of two parts. The first one concerns the creation of a dataset that contains a variety of motion features. Based on this dataset, the second one deals with the training of a DCNN-based model, which takes as input the 2D pose estimations directly computed from videos. Here we present the first training tasks and results obtained using our deep neural network model to directly estimate 3D poses. Three models were trained using the same architecture applied on several configurations of our dataset. Using a small benchmark, we evaluate our network architecture

Non Parametric Learning of Sensori-Motor Maps. Application to the Control of Multi Joint Systems

Abstract: -At the light of control and learning theories, this paper addresses the question of controlling multi-joint system using sensory feedback. A generic Sensory-Motor Control Model (SMCM) is firstly presented that solves the inverse kinematics difficulty at a theoretical level. Computational implementations of SMCM requires the knowledge of sensory motor transforms that are directly dependent to the multi-joint structure that is to be controlled. To avoid the dependency of SMCM to the analytical knowledge of these transforms, a non parametric learning approach is developed to identify non linear mappings between sensory signals and motor commands involved in SMCM. The resulting adaptive SMCM (ASMCM) is intensively tested within the scope of hand-arm reaching movements. ASMCM shows to be very effective and robust at least for this task. Its generic properties and effectiveness allow to foresee wide area of application

Enhancing the Visualization of Percussion Gestures by Virtual Character Animation

International audienceA new interface for visualizing and analyzing percussion gestures is presented, proposing enhancements of existing motion capture analysis tools. This is achieved by offering a percussion gesture analysis protocol using motion capture. A virtual character dynamic model is then designed in order to take advantage of gesture characteristics, yielding to improve gesture analysis with visualization and interaction cues of different types