Massachusetts Institute of Technology

Toyota Research Institute, Inc.

Petr Kellnhofer

Wojciech Matusik

Antonio Torralba

Adrià Recasens

William T Freeman

Frederic Durand

Simon A I Stent

Szu-Po Wang

Pitchaya Sitthi-Amorn

Piotr K Didyk

Simon Stent

A method includes receiving, with a computing device, an image, identifying one or more salient features in the image, and generating a saliency map of the image including the one or more salient features. The method further includes sampling the image based on the saliency map such that the one or more salient features are sampled at a first density of sampling and at least one portion of the image other than the one or more salient features are sampled at a second density of sampling, where the first density of sampling is greater than the second density of sampling, and storing the sampled image in a non-transitory computer readable memory.

Systems and methods for saliency-based sampling layer for neural networks

A system includes a camera positioned in an environment to capture image data of a subject; a computing device communicatively coupled to the camera, the computing device comprising a processor and a non-transitory computer-readable memory; and a machine-readable instruction set stored in the non-transitory computer-readable memory. The machine-readable instruction set causes the computing device to perform at least the following when executed by the processor: receive the image data from the camera; analyze the image data captured by the camera using a neural network trained on training data generated from a 360-degree panoramic camera configured to collect image data of a subject and a visual target that is moved about an environment; and predict a gaze direction vector of the subject with the neural network.

Opengaze: gaze-tracking in the wild

A system for determining the gaze direction of a subject includes a camera, a computing device and a machine-readable instruction set. The camera is positioned in an environment to capture image data of head of a subject. The computing device is communicatively coupled to the camera and the computing device includes a processor and a non-transitory computer-readable memory. The machine-readable instruction set is stored in the non-transitory computer-readable memory and causes the computing device to: receive image data from the camera, analyze the image data using a convolutional neural network trained on an image dataset comprising images of a head of a subject captured from viewpoints distributed around up to 360-degrees of head yaw, and predict a gaze direction vector of the subject based upon a combination of head appearance and eye appearance image data from the image dataset.

Systems and methods for gaze tracking from arbitrary viewpoints

A method and system of converting stereo video content to multi-view video content combines an Eulerian approach with a Lagrangian approach. The method comprises generating a disparity map for each of the left and right views of a received stereoscopic frame. For each corresponding pair of left and right scanlines of the received stereoscopic frame, the method further comprises decomposing the left and right scanlines into a left sum of wavelets or other basis functions, and a right sum wavelets or other basis functions. The method further comprises establishing an initial disparity correspondence between left wavelets and right wavelets based on the generated disparity maps, and refining the initial disparity between the left wavelet and the right wavelet using a phase difference between the corresponding wavelets. The method further comprises reconstructing at least one novel view based on the left and right wavelets.

3DTV at home: Eulerian-Lagrangian stereo-to-multi-view conversion

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Cambridge, MA, United States of America

Petr Kellnhofer