Samsung Electronics Co., Ltd.

Amazon Technologies, Inc.

Amin Kheradmand

Jungwon Lee

Mostafa El-Khamy

Dongwoon Bai

Haoyu Ren

Shuangquan Wang

Jiayun Wang

Himanshu Arora

Techniques for image generation based on a multi-image set and pose data is described herein. In an example, a computer system generates, by at least using a first machine learning (ML) model, a pose feature based on pose data that indicates a target pose. The computer system generates, by at least using a second ML model, a first appearance feature based on a first image and the pose data. The computer system generates, by at least using the second ML model, a second appearance feature based on a second image and the pose data. The computer system generates a combined appearance feature based on the first appearance feature and the second appearance feature. The computer system generates, by at least using a third ML model, a third image showing an appearance of an element in the target pose based on the pose feature and the combined appearance feature.

Image generation based on a multi-image set and pose data

A method and apparatus are provided. The method includes generating a dataset for real-world super resolution (SR), training a first generative adversarial network (GAN), training a second GAN, and fusing an output of the first GAN and an output of the second GAN.

Systems and method of training networks for real-world super resolution with unknown degradations

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Mountain View, CA, United States of America

Amin Kheradmand