Panasonic Intellectual Property Corporation of America

Apple Inc.

Stefano Alletto

Sotaro Tsukizawa

Yasunori Ishii

Luca Rigazio

Tanmay Batra

Bharath Kumar Comandur Jagannathan Raghunathan

In one implementation, a method of body pose estimation is performed at a device including one or more processors and non-transitory memory. The method includes obtaining a plurality of two-dimensional images of a body in a three-dimensional environment at a respective plurality of times. The method includes determining, for each of the plurality of two-dimensional images, the two-dimensional location in the two-dimensional image of one or more joints of the body at the respective plurality of times. The method includes determining, based on the two-dimensional locations, a plurality of three-dimensional locations in the three-dimensional environment of the one or more joints of the body at the respective plurality of times. The method includes determining, based on the three-dimensional locations, a plurality of updated three-dimensional locations in the three-dimensional environment of the one or more joints of the body at the respective plurality of times.

Two-stage body pose estimation

A learning method includes an input process to input, to a neural network, a first image and a second image that constitute a moving image and that are temporally adjacent to each other, where the second image is an image subsequent to the first image with a predetermined time interval therebetween, a learning process to cause the neural network to use the first image and the second image and learn to output a transformation matrix applied to all pixels of the first image and used to convert the first image into the second image, and an output process to output, as a result of estimation of motion between the first image and the second image, a motion amount image generated from the transformation matrix and representing an amount of motion of each of the pixels of the first image that continues until the predetermined time interval elapses.

Learning method and recording medium

An information processing method performed using a computer includes: obtaining first sensor data (SD) that is SD of a scene and includes noise; inputting the first SD to a single converter, and obtaining second SD outputted from the single converter as a result of denoising performed on the first SD by the single converter; obtaining third SD that is SD of a scene identical or corresponding to the scene, does not include the noise, and is different from the second SD; obtaining feature information of the second SD and feature information of the third SD, based on the second SD and the third SD, respectively; and training the single converter by machine learning using the second SD and the feature information of the second SD as converted data, and using the third SD and the feature information of the third SD as reference data corresponding to the converted data.

Information processing method, information processing apparatus, and recording medium

An information processing method includes: obtaining noise region estimation information output from a first converter by a first image including a noise region being input to the first converter; obtaining a second image, on which noise region removal processing has been performed, output from a second converter by the noise region estimation information and the first image being input to the second converter; generating a fourth image including the estimated noise region by using the noise region estimation information and a third image including no noise region and a scene corresponding to the first image; training the first converter by using machine learning in which the first image is reference data and the fourth image is conversion data; and training the second converter by using machine learning in which the third image is reference data and the second image is conversion data.

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Sunnyvale, CA, United States of America

Stefano Alletto