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The latest research from Google

MediaPipe FaceStylizer: On-device real-time few-shot face stylization

In recent years, we have witnessed rising interest across consumers and researchers in integrated augmented reality (AR) experiences using real-time face feature generation and editing functions in mobile applications, including short videos, virtual reality, and gaming. As a result, there is a growing demand for lightweight, yet high-quality face generation and editing models, which are often based on generative adversarial network (GAN) techniques. However, the majority of GAN models suffer from high computational complexity and the need for a large training dataset. In addition, it is also important to employ GAN models responsibly.

On-device content distillation with graph neural networks

World scale inverse reinforcement learning in Google Maps

Differentially private median and more

A novel computational fluid dynamics framework for turbulent flow research

TSMixer: An all-MLP architecture for time series forecasting

WeatherBench 2: A benchmark for the next generation of data-driven weather models

Modeling and improving text stability in live captions

SayTap: Language to quadrupedal locomotion

RO-ViT: Region-aware pre-training for open-vocabulary object detection with vision transformers

Responsible AI at Google Research: Perception Fairness