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Text Content

RESEARCH

Artificial Intelligence for robotics.


PUBLICATIONS

Browse over 30 years of science.


BLOG

Facts, opinions and stories.


CAREERS

Join us to pave the future with passion.


DUSt3R
READ MORE
3D RECONSTRUCTION MODELS
MADE EASY
About Us
ABOUT US
NAVER LABS EUROPE
IS THE BIGGEST INDUSTRIAL RESEARCH LAB IN ARTIFICIAL INTELLIGENCE IN FRANCE.


Whole Body Human Mesh Recovery
READ MORE
WHOLE-BODY HUMAN MESH RECOVERY
OF MULTIPLE PERSONS FROM
A SINGLE IMAGE






NEWS

Twitter @naverlabseurope      
LinkedIn NAVER LABS Europe



IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
(ICASSP) 2024

14th - 19th April 2024. 3 accepted papers. NAVER LABS Europe is co-organising
the "Self-supervision in Audio, Speech and Beyond" workshop
     
14 April 2024



SEMINAR: COMPOSITIONALITY IN VISION AND LANGUAGE

22nd April 2024.
Speaker: Ranjay Krishna is an assistant professor at the Paul G. Allen School of
Computer Science & Engineering. Please register.
22 April 2024



THE TWELFTH INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (ICLR) 2024

7th -  11th May 2024
ICLR 2024: 6 accepted papers including one spotlight. NAVER has 20 papers this
year with 3 spotlights.
7 May 2024



IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) 2024

13th - 17th May 2024. 3 accepted papers
     
13 May 2024



RECENT PUBLICATIONS


CROSS-VIEW AND CROSS-POSE COMPLETION FOR 3D HUMAN UNDERSTANDING

Matthieu Armando, Salma Galaaoui, Fabien Baradel, Thomas Lucas, Vincent Leroy,
Romain Brégier, Philippe Weinzaepfel, Gregory Rogez

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR),
Seattle, USA, 17-21 June, 2024


TASK-CONDITIONED ADAPTATION OF VISUAL FEATURES IN MULTI-TASK POLICY LEARNING

Pierre Marza, Laetitia Matignon, Olivier Simonin, Christian Wolf

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR),
Seattle, USA, 17-21 June, 2024


LABEL PROPAGATION FOR ZERO-SHOT CLASSIFICATION WITH VISION-LANGUAGE MODELS

Vladan Stojnic, Yannis Kalantidis, Georgios Tolias

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR),
Seattle, USA, 17-21 June, 2024


SLIM: SKILL LEARNING WITH MULTIPLE CRITICS

David Emukpere, Bingbing Wu, Julien Perez, Jean-Michel Renders

The IEEE International Conference on Robotics and Automation (ICRA), Yokohama,
Japan, 13-17 May, 2024


BLOG


NAVER LABS EUROPE AT ICASSP 2024



The work we're presenting this year in multi-task, multimodal and multilingual
speech processing technologies including some results made available to the
community. We’re also co-organizing the popular Self-supervision in Audio Speech
and Beyond (SASB) workshop.


3D RECONSTRUCTION MODELS MADE EASY



Dense and Unconstrained Stereo 3D Reconstruction (DUSt3R) is a novel
breakthrough approach in 3D reconstruction that works without the camera
parameters required by traditional processes.


WHOLE-BODY HUMAN MESH RECOVERY OF MULTIPLE PERSONS FROM A SINGLE IMAGE



A simple yet effective single-shot method to detect multiple people in an image
and estimate their pose, body shape and expression.




       

Reset your cookies consentAccept all cookiesPersonalise cookies

 * NAVER Corp.
 * NAVER LABS Korea
 * Privacy
 * Contact

© 2024 NAVER LABS Europe







NAVER FRANCE GENDER EQUALITY 2024

Details on the gender equality index score 2024 (related to year 2023) for NAVER
France of 87/100.

 1. Difference in female/male salary: 34/40 points
 2. Difference in salary increases female/male: 35/35 points
 3. Salary increases upon return from maternity leave: Non calculable
 4. Number of employees in under-represented gender in 10 highest salaries: 5/10
    points

The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and
Indicator n°4: +5 points in 2025) have been achieved.

—————

Index NAVER France de l’égalité professionnelle entre les femmes et les hommes
pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :

 1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
 2. Les écarts des augmentations individuelles entre les femmes et les hommes :
    35 sur 35 points
 3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
 4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes
    rémunérations : 5 sur 10 points

Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2
points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.



x


NAVER FRANCE GENDER EQUALITY 2023

Details on the gender equality index score 2024 (related to year 2023) for NAVER
France of 87/100.

1. Difference in female/male salary: 34/40 points

2. Difference in salary increases female/male: 35/35 points

3. Salary increases upon return from maternity leave: Non calculable

4. Number of employees in under-represented gender in 10 highest salaries: 5/10
points

The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and
Indicator n°4: +5 points in 2025) have been achieved.

——————-

Index NAVER France de l’égalité professionnelle entre les femmes et les hommes
pour l’année 2024 au titre des données 2023 : 87/100

 

Détail des indicateurs :

1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points

2. Les écarts des augmentations individuelles entre les femmes et les hommes :
35 sur 35 points

3. Toutes les salariées augmentées revenant de congé maternité : Incalculable

4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes
rémunérations : 5 sur 10 points

Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2
points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.



×

 * About us
 * Research
 * Code and data

 * About us

 * News
 * Seminars
 * Events
 * Collaboration
 * Awards

 * Blog
 * Careers
 * Publications

 * Research



 * Blog
 * Careers
 * Publications

NAVER LABS Europe   6-8 chemin de Maupertuis   38240 Meylan France    Contact



x


VISION


PERCEPTION TO HELP ROBOTS UNDERSTAND AND INTERACT WITH THE ENVIRONMENT. 

Visual perception is a necessary part of any intelligent system that is meant to
interact with the world. Robots need to perceive the structure, the objects, and
people in their environment to better understand the world and perform the tasks
they are assigned. Our research combines expertise in visual representation
learning, self-supervised learning and human behaviour understanding to build AI
components that help robot understand and navigate in their 3D environment,
detect and interact with surrounding objects and people and continuously adapt
themselves when deployed in new environments.



×


INTERACTION


EQUIP ROBOTS TO INTERACT SAFELY WITH HUMANS, OTHER ROBOTS AND SYSTEMS.

For a robot to be useful it must be able to represent its knowledge of the
world, share what it learns and interact with other agents, in particular
humans. Our research combines expertise in human-robot interaction, natural
language processing, speech, information retrieval, data management and low
code/no code programming to build AI components that will help next-generation
robots perform complex real-world tasks. These components will help robots
interact safely with humans and their physical environment, other robots and
systems, represent and update their world knowledge and share it with the rest
of the fleet.



×


ACTION


PROVIDING EMBODIED AGENTS WITH SEQUENTIAL DECISION-MAKING CAPABILITIES TO SAFELY
EXECUTE COMPLEX TASKS IN DYNAMIC ENVIRONMENTS. 

To make robots autonomous in real-world everyday spaces, they should be able to
learn from their interactions within these spaces, how to best execute tasks
specified by non-expert users in a safe and reliable way. To do so requires
sequential decision-making skills that combine machine learning, adaptive
planning and control in uncertain environments as well as solving hard
combinatorial optimisation problems. Our research combines expertise in
reinforcement learning, computer vision, robotic control, sim2real transfer,
large multimodal foundation models and neural combinatorial optimisation to
build AI-based architectures and algorithms to improve  robot autonomy and
robustness when completing everyday complex tasks in constantly changing
environments.



×


ACTION


To make robots autonomous in real-world everyday spaces, they should be able to
learn from their interactions within these spaces, how to best execute tasks
specified by non-expert users in a safe and reliable way. To do so requires
sequential decision-making skills that combine machine learning, adaptive
planning and control in uncertain environments as well as solving hard
combinatorial optimisation problems. Our research combines expertise in
reinforcement learning, computer vision, robotic control, sim2real transfer,
large multimodal foundation models and neural combinatorial optimisation to
build AI-based architectures and algorithms to improve  robot autonomy and
robustness when completing everyday complex tasks in constantly changing
environments.



x
VISION

The research we conduct on expressive visual representations is applicable to
visual search, object detection, image classification and the automatic
extraction of 3D human poses and shapes that can be used for human behavior
understanding and prediction, human-robot interaction or even avatar animation.
We also extract 3D information from images that can be used for intelligent
robot navigation, augmented reality and the 3D reconstruction of objects,
buildings or even entire cities.

Our work covers the spectrum from unsupervised to supervised approaches, and
from very deep architectures to very compact ones. We’re excited about the
promise of big data to bring big performance gains to our algorithms but also
passionate about the challenge of working in data-scarce and low-power
scenarios.

Furthermore, we believe that a modern computer vision system needs to be able to
continuously adapt itself to its environment and to improve itself via lifelong
learning. Our driving goal is to use our research to deliver embodied
intelligence to our users in robotics, autonomous driving, via phone cameras and
any other visual means to reach people wherever they may be.



x

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