Research

We're advancing the state-of-the-art in artificial intelligence through fundamental and applied research in open collaboration with the community.

Notable Papers

RESEARCH

ML APPLICATIONS

GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

Sean Bell

Yiqun Liu

Sami Alsheikh

Yina Tang...

KDD

COMPUTER VISION

Live Face De-Identification in Video

Oran Gafni

Lior Wolf

Yaniv Taigman

International Conference on Computer Vision (ICCV)

RESEARCH

Single-Network Whole-Body Pose Estimation

Gines Hidalgo

Yaadhav Raaj

Haroon Idrees

Donglai Xiang...

International Conference on Computer Vision (ICCV)

SPEECH & AUDIO

A Universal Music Translation Network

Noam Mor

Lior Wolf

Adam Polyak

Yaniv Taigman

International Conference on Learning Representations (ICLR)

Latest Publications

August 30, 2021

NLP

SUPERB: Speech processing Universal PERformance Benchmark

Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). …

Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee

August 30, 2021

August 09, 2021

COMPUTER VISION

GRAPHICS

Deep Relightable Appearance Models for Animatable Faces

We present a method for building high-fidelity animatable 3D face models that can be posed and rendered with novel lighting environments in real-time. …

Sai Bi, Stephen Lombardi,Shunsuke Saito, Tomas Simon, Shih-en Wei, Kevyn McPhail, Ravi Ramamoorthi, Yaser Sheikh, Jason Saragih

August 09, 2021

August 09, 2021

GRAPHICS

COMPUTER VISION

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

In two-player competitive sports, such as boxing and fencing, athletes often demonstrate efficient and tactical movements during a competition …

Jungdam Won, Deepak Gopinath, Jessica Hodgins

August 09, 2021

Fundamental & Applied Research

At Facebook AI, we conduct both fundamental and applied research to advance our understanding and impact product experiences. We publish our discoveries in peer reviewed academic journals and conferences, and build AI technologies used by billions of people around the world.

Fundamental Research

FAIR seeks to further our fundamental understanding in both new and existing domains, covering the full spectrum of topics related to AI, with the mission of advancing the state-of-the-art of AI through open research for the benefit of all.

Along with the key principles of Facebook AI - openness, collaboration, excellence, and scale - we believe FAIR researchers also need to have the freedom and autonomy to design and follow their own research agendas so they can take on the most impactful work and develop the most disruptive projects, all while sharing their results with the community.

Applied Research

Facebook AI Applied Research engages in cutting-edge research that can improve and power new product experiences at huge scale for our community. Building on Facebook AI's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on building social value and bringing the world closer together.

Our Values

We align our fundamental and applied research efforts and applications around a few key principles:

Openness

We believe the latest advancements in AI should be published and open-sourced for the community to learn about and build upon.

Collaboration

We collaborate openly with both internal and external partners to share knowledge and cultivate diverse perspectives and needs.

Excellence

There is no shortage of new areas to explore in AI - our researchers focus on the projects that we believe will have the most positive impact on people and society.

Scale

To bring the benefits of AI to more people and improve accessibility, our research must account for both large scale data and computation needs.

Request for Proprosals

Facebook AI is pleased to invite university faculty to submit proposals that will help accelerate research on interpretable personalized recommendations using machine learning on graph data.

Help Us Pioneer the Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.