Disney Research Pittsburgh

We present a solution for monitoring nocturnal giraffe behavior by reducing several hours of thermal camera surveillance footage into a short video summary which can be reviewed by experts.

We combine this information with tracked player positions to build a structured predictor. Given unseen player positions from a new game, we use the learned predictor to generate target pan-tilt-zoom values for a robotic camera.

REVEL is an augmented reality (AR) tactile technology that al-lows for change to the tactile feeling of real objects by augmenting them with virtual tactile textures using a device worn by the user.

We consider the problem of learning predictive models for in-game sports play prediction. Focusing on basketball, we develop models for anticipating near-future events given the current game state. We employ a latent factor modeling approach, which leads to a compact data representation that enables efficient prediction given raw spatiotemporal tracking data.

In this paper, given an entire season’s worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis.

Large scale structure-from-motion (SfM) algorithms have recently enabled us to reconstruct highly detailed 3-D models of our surroundings simply by taking photographs. In this paper, we propose to leverage these reconstruction techniques and automatically estimate the outdoor illumination conditions for each image in a SfM photo collection.

We present a method for transforming a 3D object into a cube or a box using a continuous folding sequence.

We present a method to deconstruct the content of an actor’s facial expression into three layers using an additive composition function, transfer the content to parameter-parallel layers for the character, and reconstruct the character’s expression using the same composition function.