Microsoft Technology Licensing, LLC

Jonathan James Taylor

Jamie Daniel Joseph Shotton

Andrew William Fitzgibbon

Shahram Izadi

Pushmeet Kohli

Toby Leonard Sharp

Cem Keskin

Duncan Paul Robertson

Reinhard Sebastian Bernhard Nowozin

Matthew Darius Cook

Model fitting from raw time of flight image data is described, for example, to track position and orientation of a human hand or other entity. In various examples, raw image data depicting the entity is received from a time of flight camera. A 3D model of the entity is accessed and used to render, from the 3D model, simulations of raw time of flight image data depicting the entity in a specified pose/shape. The simulated raw image data and at least part of the received raw image data are compared and on the basis of the comparison, parameters of the entity are computed.

Model fitting from raw time-of-flight images

Tracking hand or body pose from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a prediction engine takes a single frame of image data and predicts a distribution over a pose of a hand or body depicted in the image data. In examples, a stochastic optimizer has a pool of candidate poses of the hand or body which it iteratively refines, and samples from the predicted distribution are used to replace some candidate poses in the pool. In some examples a best candidate pose from the pool is selected as the current tracked pose and the selection processes uses a 3D model of the hand or body.

Tracking hand/body pose

Computing pose and/or shape of a modifiable entity is described. In various embodiments a model of an entity (such as a human hand, a golf player holding a golf club, an animal, a body organ) is fitted to an image depicting an example of the entity in a particular pose and shape. In examples, an optimization process finds values of pose and/or shape parameters that when applied to the model explain the image data well. In examples the optimization process is influenced by correspondences between image elements and model points obtained from a regression engine where the regression engine may be a random decision forest. For example, the random decision forest may take elements of the image and calculate candidate correspondences between those image elements and model points. In examples the model, pose and correspondences may be used for control of various applications including computer games, medical systems, augmented reality.

Computing pose and/or shape of modifiable entities

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Jonathan James Taylor