We present an algorithmic framework for the early classification of human intentions, and use it to accurately predict future human motions when planning the path of a robot in an environment that is shared with humans.
By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge.
In this paper, we consider the problem of creating rich and varied conversational behaviors for data-driven animation of walking and jogging characters.
We propose and demonstrate the viability of using OAM to create an automultiscopic 3D display.
In this article, we conduct a series of experiments to evaluate the distinctiveness and attractiveness of human motions (face and body) and voices.
We propose a new approach based on finding latent Path Patterns in both real and simulated data in order to analyze and compare them.
In this paper, we present a set of experiments in which we explore some factors that contribute to the perception of cloth, to determine how efficiency could be improved without sacrificing realism.
We show that with selective coding OAM states, off‐axis points and spatial variables encoded with OAM are reproducible.
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