Snap Inc.

Dhritiman Sagar

Andrew James McPhee

Samuel Edward Hare

Ebony James Charlton

Sumant Milind Hanumante

Daniel Moreno

Kyle Goodrich

Tony Mathew

Maxim Maximov Lazarov

Wentao Shang

Ilteris Kaan Canberk

Zhou Ren

Victor Shaburov

Robert Cornelius Murphy

Eitan Pilipski

Jokubas Dargis

Peicheng Yu

Daniel Vincent Grippi

Ning Xu

Shenlong Wang

Chongyang Ma

Kun Duan

Mathieu Emmanuel Vignau

Daniel Ron

Stanislav Minakov

Michael David Marr

Kai Chen

Jun Huang

Michael Ronald Cieslak

Farnaz Azmoodeh

Jiayao Yu

Zahra Ferdowsi

Sergey Tulyakov

David Meisenholder

Jian Ren

Aleksei Stoliar

James Powderly

David Ben Ezra

Qing Jin

Ju Hu

Huan Wang

Yanyu Li

Justin Wilder

Ergeta Muca

Colin Eles

Pavlo Chemerys

Andrew James Mcphee

Qi Pan

Ozi Egri

Eyal Zak

Described is a system for improving machine learning models. In some cases, the system improves such models by identifying a performance characteristic for machine learning model blocks in an iterative denoising process of a machine learning model, connecting a prior machine learning model block with a subsequent machine learning model block of the machine learning model blocks within the machine learning model based on the identified performance characteristic, identifying a prompt of a user, the prompt indicative of an intent of the user for generative images, and analyzing data corresponding to the prompt using the machine learning model to generate one or more images, the machine learning model trained to generate images based on data corresponding to prompts.

Text-to-image diffusion model rearchitecture

The subject technology applies, to image data and depth data, a 3D effect including at least one beautification operation based on an augmented reality content generator, the 3D effect including a beautification operation, the beautification operation comprising modifying image data, the image data including a region corresponding to a representation of a face, the beautification operation comprising using a machine learning model for at least one of smoothing blemishes or preserving facial skin texture. The subject technology generates a depth map using at least the depth data. The subject technology generates a segmentation mask based at least on the image data. The subject technology performs background inpainting and blurring of the image data using at least the segmentation mask to generate background inpainted image data. The subject technology generates a 3D message based at least in part on the applied 3D effect including the at least one beautification operation.

Beautification techniques for 3D data in a messaging system

Augmented reality (AR) and virtual reality (VR) environment enhancement using an eyewear device. The eyewear device includes an image capture system, a display system, and a position detection system. The image capture system and position detection system identify feature points within a point cloud that represents captured images of an environment. The display system presents image overlays to a user including enhancement graphics positioned at the feature points within the environment.

Augmented reality environment enhancement

The subject technology applies a three-dimensional (3D) effect to image data and depth data based at least in part on an augmented reality content generator. The subject technology generates a segmentation mask based at least on the image data. The subject technology performs background inpainting and blurring of the image data using at least the segmentation mask to generate background inpainted image data. The subject technology generates a packed depth map based at least in part on the a depth map of the depth data. The subject technology generates, using the processor, a message including information related to the applied 3D effect, the image data, and the depth data.

Generating 3D data in a messaging system

A context based augmented reality system can be used to display augmented reality elements over a live video feed on a client device. The augmented reality elements can be selected based on a number of context inputs generated by the client device. The context inputs can include location data of the client device and location data of nearby physical places that have preconfigured augmented elements. The preconfigured augmented elements can be preconfigured to exhibit a design scheme of the corresponding physical place.

Virtual vision system

A venue system of a client device can submit a location request to a server, which returns multiple venues that are near the client device. The client device can use one or more machine learning schemes (e.g., convolutional neural networks) to determine that the client device is located in one of specific venues of the possible venues. The venue system can further select imagery for presentation based on the venue selection. The presentation may be published as ephemeral message on a network platform.

Device location based on machine learning classifications

The subject technology receives, at a client device, a selection of a selectable graphical item from a plurality of selectable graphical items, the selectable graphical item comprising an augmented reality content generator including a 3D effect. The subject technology applies, to image data and depth data, the 3D effect based at least in part on the augmented reality content generator, the applying the 3D effect. The subject technology generates a depth map using at least the depth data, generates a segmentation mask based at least on the image data, and performs background inpainting and blurring of the image data using at least the segmentation mask to generate background inpainted image data. The subject technology generates a 3D message based at least in part on the applied 3D effect.

Effects for 3D data in a messaging system

A content display system can control which content and how the content is displayed based on viewing parameters, such as a map zoom level, and physical distance parameters, e.g., a geo-fence distance and an icon visibility distance. Different combinations of input (e.g., zoom level and physical distances) yield a myriad of pre-set content displays on the client device, thereby allowing a creator of an icon to finely tune how content displayed otherwise accessed.

Mobile-based cartographic control of display content

A redundant tracking system comprising multiple redundant tracking sub-systems, enabling seamless transitions between such tracking sub-systems, provides a solution to this problem by merging multiple tracking approaches into a single tracking system. This system is able to combine tracking objects with six degrees of freedom (6 DoF) and 3 DoF through combining and transitioning between multiple tracking systems based on the availability of tracking indicia tracked by the tracking systems. Thus, as the indicia tracked by any one tracking system becomes unavailable, the redundant tracking system seamlessly switches between tracking in 6 DoF and 3 DoF thereby providing the user with an uninterrupted experience.

Redundant tracking system

Augmented reality experiences of a user wearing an electronic eyewear device are captured by at least one camera on a frame of the electronic eyewear device, the at least one camera having a field of view that is larger than a field of view of a display of the electronic eyewear device. An augmented reality feature or object is applied to the captured scene. A photo or video of the augmented reality scene is captured and a first portion of the captured photo or video is displayed in the display. The display is adjusted to display a second portion of the captured photo or video with the augmented reality features as the user moves the user's head to view the second portion of the captured photo or video. The captured photo or video may be transferred to another device for viewing the larger field of view augmented reality image.

Extended field-of-view capture of augmented reality experiences

The subject technology generates depth data using a machine learning model based at least in part on captured image data from at least one camera of a client device. The subject technology applies, to the captured image data and the generated depth data, a 3D effect based at least in part on an augmented reality content generator. The subject technology generates a depth map using at least the depth data. The subject technology generates a packed depth map based at least in part on the depth map, the generating the packed depth map. The subject technology converts a single channel floating point texture to a raw depth map. The subject technology generates multiple channels based at least in part on the raw depth map. The subject technology generates a segmentation mask based at least on the captured image data. The subject technology performs background inpainting and blurring of the captured image data using at least the segmentation mask to generate background inpainted image data.

Providing 3D data for messages in a messaging system

Systems, devices, media, and methods are presented for displaying a virtual object on the display of a portable eyewear device using motion data gathered by a face-tracking application on a mobile device. A controller engine leverages the processing power of a mobile device to locate the face supporting the eyewear, locate the hand holding the mobile device, acquire the motion data, and calculate an apparent path of the virtual object. The virtual object is displayed in a series of locations along the apparent path, based on both the course traveled by the mobile device (in the hand) and the track traveled by the eyewear device (on the face), so that the virtual object is persistently viewable to the user.

Virtual object display interface between a wearable device and a mobile device

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Marina Del Rey, CA, United States of America

Dhritiman Sagar