Abstract: A method for virtually representing human body poses includes receiving positioning data detailing parameters of one or more body parts of a human user based at least in part on input from one or more sensors. One or more mapping constraints are maintained that relate a model articulated representation to a target articulated representation. A model pose of the model articulated representation and a target pose of the target articulated representation are concurrently estimated based at least in part on the positioning data and the one or more mapping constraints. The previously-trained pose optimization machine is trained with training positioning data having ground truth labels for the model articulated representation. The target articulated representation is output for display with the target pose as a virtual representation of the human user.

Abstract: A method for virtually representing human body poses includes receiving positioning data detailing parameters of one or more body parts of a human user based at least in part on input from one or more sensors. One or more mapping constraints are maintained that relate a model articulated representation to a target articulated representation. A model pose of the model articulated representation and a target pose of the target articulated representation are concurrently estimated based at least in part on the positioning data and the one or more mapping constraints. The previously-trained pose optimization machine is trained with training positioning data having ground truth labels for the model articulated representation. The target articulated representation is output for display with the target pose as a virtual representation of the human user.

Abstract: A method for predicting the pose of an articulated object includes receiving spatial information for n joints of the articulated object. The spatial information for the n joints is passed to a machine learning model previously trained to receive spatial information for n+m joints as input, wherein m>=1. From the machine learning model, a pose prediction for the articulated object is received as output based at least on the spatial information for the n joints, and without spatial information for the m joints.

Abstract: One example provides a system for reading birefringent data. The system comprises one or more light sources, a first polarization state generator positioned to generate first polarized light from light of a first wavelength band output by the one or more light sources, a second polarization state generator positioned to generate second polarized light from light of a second wavelength band output by the one or light sources, an image sensor configured to acquire an image of the sample region via the first polarized light and the second polarized light, a polarization state analyzer disposed between the sample region and the image sensor, a first bandpass filter configured to pass light of the first wavelength band onto the image sensor, and a second bandpass filter configured to pass light of the second wavelength band onto the image sensor.

Abstract: One example provides a system for reading birefringent data. The system comprises one or more light sources, a first polarization state generator positioned to generate first polarized light from light of a first wavelength band output by the one or more light sources, a second polarization state generator positioned to generate second polarized light from light of a second wavelength band output by the one or light sources, an image sensor configured to acquire an image of the sample region via the first polarized light and the second polarized light, a polarization state analyzer disposed between the sample region and the image sensor, a first bandpass filter configured to pass light of the first wavelength band onto the image sensor, and a second bandpass filter configured to pass light of the second wavelength band onto the image sensor.

Abstract: One example provides a system for reading birefringent data. The system comprises one or more light sources, a first polarization state generator positioned to generate first polarized light from light of a first wavelength band output by the one or more light sources, a second polarization state generator positioned to generate second polarized light from light of a second wavelength band output by the one or light sources, an image sensor configured to acquire an image of the sample region via the first polarized light and the second polarized light, a polarization state analyzer disposed between the sample region and the image sensor, a first bandpass filter configured to pass light of the first wavelength band onto the image sensor, and a second bandpass filter configured to pass light of the second wavelength band onto the image sensor.

Abstract: One example provides a system for reading birefringent data. The system comprises one or more light sources, a first polarization state generator positioned to generate first polarized light from light of a first wavelength band output by the one or more light sources, a second polarization state generator positioned to generate second polarized light from light of a second wavelength band output by the one or light sources, an image sensor configured to acquire an image of the sample region via the first polarized light and the second polarized light, a polarization state analyzer disposed between the sample region and the image sensor, a first bandpass filter configured to pass light of the first wavelength band onto the image sensor, and a second bandpass filter configured to pass light of the second wavelength band onto the image sensor.