Abstract. This paper presents a study of the dynamic coupling between a user and a virtual character during body interaction. Coupling is directly linked with other dimensions, such as co-presence, engagement, and believability, and was measured in an experiment that allowed users to describe their subjective feelings about those dimensions of interest. The experiment was based on a theatrical game involving the imitation of slow upper-body movements and the proposal of new movements by the user and virtual agent. The agent's behaviour varied in autonomy: the agent could limit itself to imitating the user's movements only, initiate new movements, or combine both behaviours. After the game, each participant completed a questionnaire regarding their engagement in the interaction, their subjective feeling about the co-presence of the agent, etc. Based on four main dimensions of interest, we tested several hypotheses against our experimental results, which are discussed here.

International audienc

In our context of Virtual Theater, a virtual actor performs with a real actor. They communicate through movements and choreography. The system has to interpret the real actor's gesture into a symbolic representation. Therefore, we present a method for real-time recognition. We use properties from Principal Component Analysis (PCA) to create signature for each gesture and a multiagent system to perform the recognition.

No abstract

Neural Architecture Search (NAS) algorithms are used to automate the design of deep neural networks. Finding the best architecture for a given dataset can be time consuming since these algorithms have to explore a large number of networks, and score them according to their performances to choose the most appropriate one. In this work, we propose a novel metric that uses the Intra-Cluster Distance (ICD) score to evaluate the ability of an untrained model to distinguish between data in order to approximate its quality. We also use an improved version of the FireFly algorithm, more robust to the local optimums problem than the baseline FireFly algorithm, as a search technique to find the best neural network model adapted to a specific dataset. Experimental results on the different NAS Benchmarks show that our metric is valid for either scoring CNNs and RNNs, and that our proposed FireFly algorithm can improve the result obtained by the state-of-art training-free methods.