Snap Inc.

Istvan Marton

Sergey Demyanov

Aleksei Podkin

Yunqing Hu

Daniil Ostashev

Michael David Marr

Kai Chen

Jun Huang

Michael Ronald Cieslak

Farnaz Azmoodeh

Jiayao Yu

Menglei Chai

Fedir Poliakov

Aleksei Stoliar

Vladislav Shakhrai

Cristian Zaloj

The subject technology receives an object mesh, information related to a viewpoint for rendering an image of an object having a reflective surface, and a set of maps. The subject technology generates a rasterized RGB (Red Green Blue) image based on the object mesh, the viewpoint, and the set of maps. The subject technology generates, using a neural network model, an output image of the object with the reflective surface based at least in part on the rasterized RGB image and the viewpoint. The subject technology provides for display the output image of the object with the reflective surface on a display of a computer client device.

Neural shading of reflective surfaces

A method for applying lighting conditions to a virtual object in an augmented reality (AR) device is described. In one aspect, the method includes generating, using a camera of a mobile device, an image, accessing a virtual object corresponding to an object in the image, identifying lighting parameters of the virtual object based on a machine learning model that is pre-trained with a paired dataset, the paired dataset includes synthetic source data and synthetic target data, the synthetic source data includes environment maps and 3D scans of items depicted in the environment map, the synthetic target data includes a synthetic sphere image rendered in the same environment map, applying the lighting parameters to the virtual object, and displaying, in a display of the mobile device, the shaded virtual object as a layer to the image.

Light estimation method for three-dimensional (3D) rendered objects

Systems and embodiments herein describe an augmented reality (AR) object rendering system. The AR object rendering system receives an image, generates a set of noise parameters and a set of blur parameters for the image using a neural network trained on a paired dataset of images, identifies an AR object associated with the image, modifies the AR object using the set of noise parameters and the set of blur parameters, displays the modified augmented reality object within the image.

Augmented reality object rendering based on camera quality

Systems, devices, media, and methods are presented for determining performance metrics of client devices on a network using benchmark applications. Benchmark applications are distributed to client devices to produce performance metrics for the client devices. Performance metrics of the client devices received from the client devices are grouped to obtain performance metrics for different types of client devices.

Network device performance metrics determination

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Encino, CA, United States of America

Istvan Marton