News
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January 2022: "Filtered-CoPhy" got accepted to ICLR'22! Congrats Steeven Janny!
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October 2021: Our paper "Leveraging MoCap Data for Human Mesh Recovery" is accepted to 3DV'21!
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May 2021: Our research conducted during my PhD got featured by INSA Lyon.
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April 2021: I received the runner-up thesis prize from AFRIF for my PhD manuscript!
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September 2020: I started as a research scientist at Naver Labs Europe in Grenoble, France.
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June 2020: I successfully defended my PhD!
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Publications
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Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Steeven Janny,
Fabien Baradel,
Natalia Neverova,
Madiha Nadri,
Greg Mori,
Christian Wolf
ICLR, 2022
PDF
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OpenReview
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Project Page
We propose a model learned in a unsupervised manner which is able to perform counterfactual predictions in pixel space.
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Leveraging MoCap Data for Human Mesh Recovery
Fabien Baradel*,
Thibault Groueix*,
Philippe Weinzaepfel,
Romain Brégier,
Yannis Kalantidis,
Grégory Rogez
3DV, 2021  
PDF
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arXiv
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Video-short
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Video-long
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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.
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CoPhy: Counterfactual Learning of Physical Dynamics
Fabien Baradel,
Natalia Neverova,
Julien Mille,
Greg Mori,
Christian Wolf
ICLR, 2020 (Spotlight
presentation)
PDF
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arXiv
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Code-Dataset
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Video
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bibtex
We introduce a new problem of counterfactual learning of object mechanics from visual input
and a benchmark called CoPhy.
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Learning Video Representations using Contrastive Bidirectional Transformer
Chen Sun,
Fabien Baradel,
Kevin Murphy,
Cordelia Schmid
arXiv preprint, 2019
PDF
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arXiv
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bibtex
Self-supervised video representation by leveraging ASR and long videos via noise contrastive
estimation.
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Object Level Visual Reasoning in Videos
Fabien Baradel,
Natalia Neverova,
Christian Wolf,
Julien Mille,
Greg Mori
ECCV, 2018
Project page
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PDF
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arXiv
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video
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bibtex
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Code
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Complementary Mask Data
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Poster
A model capable of learning to reason about semantically meaningful spatio-temporal
interactions in videos.
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Human Activity Recognition with Pose-driven Attention to RGB
Fabien Baradel,
Christian Wolf,
Julien Mille
BMVC, 2018
PDF
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bibtex
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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.
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Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Fabien Baradel,
Christian Wolf,
Julien Mille,
Graham Taylor
CVPR, 2018
PDF
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arXiv
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project page
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video
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bibtex
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CVPR Daily
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Code
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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.
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Human Action Recognition: Pose-based Attention draws focus to Hands
Fabien Baradel,
Christian Wolf,
Julien Mille
ICCV, Workshop "Hands in Action", 2017
PDF
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bibtex
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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.
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Discrepancy-based networks for unsupervised domain adaptation: a comparative
study
Gabriela
Csurka,
Fabien Baradel,
Boris
Chidlovskii,
Stephane
Clinchant,
ICCV, Workshop "Task-CV", 2017
PDF
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bibtex
We introduce a new dataset for Domain Adaptation and show a comparaison between shallow and
deep methods based on Maximum Mean Discrepancy.
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Pose-conditioned Spatio-Temporal Attention for Human Action Recognition
Fabien Baradel,
Christian Wolf,
Julien Mille
arXiv preprint, 2017
arXiv
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PDF
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project page
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video
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bibtex
We introduce an attention-based mechanism around hands on RGB videos conditioned on features
extracted from human 3D
pose.
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PhD Thesis
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Structured Deep Learning for Video Analysis
Fabien Baradel
Université de Lyon - INSA Lyon, 2020
Runner-up thesis prize - AFRIF
PDF
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video
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slides-pdf
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slides-pptx
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bibtex
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Patents
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Conditional adaptation network for image classification
Fabien Baradel,
Gabriela
Csurka,
Boris
Chidlovskii,
US Patent App. 15/450,620 - Xerox Corp, 2017
PDF
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bibtex
We introduce a new method based on Conditional Maximum Mean Discrepancy for domain adaptation
on image classification.
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Reviewer
ICCV 2021, ECCV 2020, CVPR 2019-2020, ICML 2019, NIPS 2018, IJCV, TPAMI
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