Human facial expressions change dynamically, so their recognition / analysis should be conducted by accounting for the temporal evolution of face deformations either in 2D or 3D. While abundant 2D video data do exist, this is not the case in 3D, where few 3D dynamic (4D) datasets were released for public use. The negative consequence of this scarcity of data is amplified by current deep learning based-methods for facial expression analysis that require large quantities of variegate samples to be effectively trained. With the aim of smoothing such limitations, in this paper we propose a large dataset, named Florence 4D, composed of dynamic sequences of 3D face models, where a combination of synthetic and real identities exhibit an unprecedented variety of 4D facial expressions, with variations that include the classical neutralapex transition, but generalize to expression-to-expression. All these characteristics are not exposed by any of the existing 4D datasets and they cannot even be obtained by combining more than one dataset. We strongly believe that making such a data corpora publicly available to the community will allow designing and experimenting new applications that were not possible to investigate till now. To show at some extent the difficulty of our data in terms of different identities and varying expressions, we also report a baseline experimentation on the proposed dataset that can be used as baseline.
NeuronUnityIntgration2.0 1 is a plugin for Unity which provides gesture recognition functionalities through the Perception Neuron motion capture suit. The system offers a recording mode, which guides the user through the collection of a dataset of gestures, and a recognition mode, capable of detecting the recorded actions in real time. Gestures are recognized by training Support Vector Machines directly within our plugin. We demonstrate the effectiveness of our application through an experimental evaluation on a newly collected dataset. Furthermore, external applications can exploit NeuronUnityIntgration2.0's recognition capabilities thanks to a set of exposed API. CCS CONCEPTS• Human-centered computing → Gestural input; Virtual reality; Interactive systems and tools.
In this demo we present two applications designed for the cultural heritage domain that exploit gamification techniques in order to improve fruition and learning of museum artworks. The two applications encourage users to replicate the poses and facial expressions of characters from paintings or statues, to help museum visitors make connections with works of art. Both applications challenge the user to fulfill a task in a funny way and provide the user with a visual report of the his/her experience that can be shared on social media, improving the engagement of the museums, and providing information on the artworks replicated in the challenge. CCS CONCEPTS• Software and its engineering → Interactive games; • Computing methodologies → Computer vision.
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