In this article we present a multichannel animation system for producing utterances signed in French Sign Language (LSF) by a virtual character. The main challenges of such a system are simultaneously capturing data for the entire body, including the movements of the torso, hands, and face, and developing a data-driven animation engine that takes into account the expressive characteristics of signed languages. Our approach consists of decomposing motion along different channels, representing the body parts that correspond to the linguistic components of signed languages. We show the ability of this animation system to create novel utterances in LSF, and present an evaluation by target users which highlights the importance of the respective body parts in the production of signs. We validate our framework by testing the believability and intelligibility of our virtual signer.
Abstract-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 that 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. Furthermore, the positive definiteness is maintained when a symmetric corridor is used to reduce the search space and thus the algorithmic complexity, which, is quadratic in the worse case . The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to the conclusion that, when the pairwise distance matrix obtained from the training data is far from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for several classical elastic distances we have tested.
In this paper we present a review of computable descriptors of human motion. We first present low-level descriptors that compute quantities directly from the raw motion data. We then present higher level descriptors that use low-level ones to compute boolean, single value or continuous quantities that can be interpreted, automatically or manually, to qualify the meaning, style or expressiveness of a motion. We provide formulas inspired from the state of the art that can be applied to 3D motion capture data.
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