Fabien Baradel

I am a research scientist at Naver Labs Europe working on computer vision and machine learning. I did my PhD at INSA Lyon advised by Christian Wolf and Julien Mille. I have also spent time at Google, Simon Fraser University and University of Guelph during my PhD journey. I received my Engineer's degree (MSc) from ENSAI.

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  • October 2022: Our paper "Leveraging MoCap Data for Human Mesh Recovery" is accepted to 3DV'21!
  • May 2021: Our research conducted during my PhD got featured by INSA Lyon.
  • April 2021: I received the runner-up thesis prize from AFRIF for my PhD manuscript!
  • September 2020: I started as a research scientist at Naver Labs Europe in Grenoble, France.
  • June 2020: I successfully defended my PhD!

Leveraging MoCap Data for Human Mesh Recovery
Fabien Baradel*, Thibault Groueix*, Philippe Weinzaepfel, Romain Brégier, Yannis Kalantidis, Grégory Rogez
3DV, 2021  
PDF / arXiv / bibtex

We show that Mocap data can be used for improving image-based and video-based human mesh recovery methods. We propose a video-based transformer model called PoseBERT which is trained on synthetic data only.


CoPhy: Counterfactual Learning of Physical Dynamics
Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, Christian Wolf
ICLR, 2020   (Spotlight presentation)
PDF / arXiv / Code-Dataset / Video / bibtex

We introduce a new problem of counterfactual learning of object mechanics from visual input and a benchmark called CoPhy.


Learning Video Representations using Contrastive Bidirectional Transformer
Chen Sun, Fabien Baradel, Kevin Murphy, Cordelia Schmid
arXiv preprint, 2019
PDF / arXiv / bibtex

Self-supervised video representation by leveraging ASR and long videos via noise contrastive estimation.


Object Level Visual Reasoning in Videos
Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille, Greg Mori
ECCV, 2018
Project page / PDF / arXiv / video / bibtex / Code / Complementary Mask Data / Poster

A model capable of learning to reason about semantically meaningful spatio-temporal interactions in videos.


Human Activity Recognition with Pose-driven Attention to RGB
Fabien Baradel, Christian Wolf, Julien Mille
BMVC, 2018
PDF / bibtex / Poster

Human activity recogntion using skeleton data and RGB. We propose a network able to focus on relevant parts of the RGB stream given deep features extracted from the pose stream.


Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Fabien Baradel, Christian Wolf, Julien Mille, Graham Taylor
CVPR, 2018
PDF / arXiv / project page / video / bibtex / CVPR Daily / Code / Poster

We propose a new method for human action recognition relying on RGB data only. A visual attention module is able to extract glimpses within each frame. Resulting local descriptors are soft-assigned to distributed workers which are finally classifying the video.


Human Action Recognition: Pose-based Attention draws focus to Hands
Fabien Baradel, Christian Wolf, Julien Mille
ICCV, Workshop "Hands in Action", 2017
PDF / bibtex / Poster

A new spatio-temporal attention based mechanism for human action recognition able to automatically attend to most important human hands and detect the most discriminative moments in an action.


Discrepancy-based networks for unsupervised domain adaptation: a comparative study
Gabriela Csurka, Fabien Baradel, Boris Chidlovskii, Stephane Clinchant,
ICCV, Workshop "Task-CV", 2017
PDF / bibtex

We introduce a new dataset for Domain Adaptation and show a comparaison between shallow and deep methods based on Maximum Mean Discrepancy.


Pose-conditioned Spatio-Temporal Attention for Human Action Recognition
Fabien Baradel, Christian Wolf, Julien Mille
arXiv preprint, 2017
arXiv / PDF / project page / video / bibtex

We introduce an attention-based mechanism around hands on RGB videos conditioned on features extracted from human 3D pose.

PhD Thesis

Structured Deep Learning for Video Analysis
Fabien Baradel
Université de Lyon - INSA Lyon, 2020
Runner-up thesis prize - AFRIF
PDF / video / slides-pdf / slides-pptx / bibtex


Conditional adaptation network for image classification
Fabien Baradel, Gabriela Csurka, Boris Chidlovskii,
US Patent App. 15/450,620 - Xerox Corp, 2017
PDF / bibtex

We introduce a new method based on Conditional Maximum Mean Discrepancy for domain adaptation on image classification.

Reviewer ICCV 2021, ECCV 2020, CVPR 2019-2020, ICML 2019, NIPS 2018, IJCV, TPAMI

Machine Learning
Science U - M2 Info - 28h30 (CM+TP) - 2018/2019
Github repo / Slides: 1 - 2 / Exercises: 1 - 2

Regression Modelling
Univ Lyon 1 - M2 Data Science - 12h (TP) - Fall 2017

Probability & Statistics
Univ Lyon 1 - L2 Info & Maths-Eco - 12h+8h (TP) - Fall 2017

EPITA - 1st year - 24h (CM+TD) - September 2017

Introduction to Deep Learning with Tensorflow
ENSAI - MSc Data Science - 6h - January 2017
Github repo / slides

Awesome webpage...