Disney Research Pittsburgh

Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game.

We present an integrated approach to generate three levels of 3D proxies: single-points, polygonal shapes, and a full joint hierarchy.

In this paper, we conduct an experiment to verify if artists are successful in their goal of leading viewer gaze.

This paper presents an automatic model reduction procedure for humanoid robots, which is task-specific.

In this paper, we demonstrate a successful application of an iterative learning control algorithm to automate the process of fine tuning choreographed human-speed motions on a 37 degree-of-freedom humanoid robot.

We describe the conceptual design, architecture, and implementation of a multimodal, robot-child dialogue system in a fast-paced, speech-controlled collaborative game.

We present initial findings from an experiment in which participants played Mafia, an established role-playing game, with our robot.

In this paper, we present a method which accurately estimates the likelihood of chances in soccer using strategic features from an entire season of player and ball tracking data taken from a professional league.

In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections.

In this paper, we give an overview of the types of analysis currently performed mostly with hand-labeled event data and highlight the problems associated with the influx of spatiotemporal data.