no code implementations • LREC 2022 • Clémence Mertz, Vincent Barreaud, Thibaut Le Naour, Damien Lolive, Sylvie Gibet
The automatic translation of sign language videos into transcribed texts is rarely approached in its whole, as it implies to finely model the grammatical mechanisms that govern these languages.
no code implementations • 16 Oct 2022 • Mansour Tchenegnon, Sylvie Gibet, Thibaut Le Naour
We propose a new loss function that we call Laplacian loss, based on spatio-temporal Laplacian representation of the motion as a graph.
no code implementations • LREC 2020 • Lucie Naert, Caroline Larboulette, Sylvie Gibet
To overcome this lack of authenticity, solutions in which the avatar is animated from motion capture data are promising.
no code implementations • 23 Nov 2016 • Pierre-François Marteau, Sylvie Gibet, Clément Reverdy
In return, very few of these methods have explicitly addressed the dimensionality reduction along the time axis.
no code implementations • 18 Aug 2014 • Pierre-François Marteau, Sylvie Gibet, Clement Reverdy
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.
no code implementations • LREC 2014 • Demulier Virginie, Elisabetta Bevacqua, Florian Focone, Tom Giraud, Pamela Carreno, Brice Isableu, Sylvie Gibet, Pierre De Loor, Jean-Claude Martin
Recent technologies enable the exploitation of full body expressions in applications such as interactive arts but are still limited in terms of dyadic subtle interaction patterns.
no code implementations • 27 May 2010 • Pierre-François Marteau, Sylvie Gibet
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.