Disney Research Los Angeles

We present a system enabling users to accurately catch a real ball while immersed in a virtual reality environment.

The hierarchical and heterogeneous nature of human crowd behaviors makes it very difficult to find a definitive set of evaluation rules or empirical metrics. Therefore, data-driven evaluation methods are particularly useful for this purpose.

We propose ways to increase the displayed image pixel resolution without having to manufacture any additional pixels, while at the same time maintaining full-color 24-bit images.

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.