Snap Inc.

Huan Wang

Menglei Chai

Kyle Olszewski

Zeng Huang

Sergey Tulyakov

Dhritiman Sagar

Jian Ren

Aleksei Stoliar

Qing Jin

Ju Hu

Yanyu Li

Ergeta Muca

Colin Eles

Pavlo Chemerys

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

Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.

3D modeling based on neural light field

Growing community of inventors

Somerville, MA, United States of America

Huan Wang