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Jean-Marc Odobez

Related publications (203)

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Robust Unsupervised Gaze Calibration Using Conversation and Manipulation Attention Priors

Jean-Marc Odobez, Rémy Alain Siegfried

Gaze estimation is a difficult task, even for humans. However, as humans, we are good at understanding a situation and exploiting it to guess the expected visual focus of attention of people, and we usually use this information to retrieve people's gaze. I ...

ASSOC COMPUTING MACHINERY2022

A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

Jean-Marc Odobez, Anshul Gupta, Samy Tafasca

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to ...

IEEE2022

Active Learning of Bayesian Probabilistic Movement Primitives

Jean-Marc Odobez, Sylvain Calinon, Hakan Girgin, Thibaut Antoine Kulak

Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distribu ...

IEEE2021

An Attention Mechanism for Deep Q-Networks with Applications in Robotic Pushing

Jean-Marc Odobez, Sylvain Calinon

Humans effortlessly solve push tasks in everyday life but unlocking these capabilities remains a research challenge in robotics. Physical models are often inaccurate or unattainable. State-of-the-art data-driven approaches learn to compensate for these ina ...

Idiap2021

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This paper introduces a novel approach for extracting speaker embeddings from audio mixtures of multiple overlapping voices. This approach is based on a multi-task neural network. The network first extracts a latent feature for each direction. This feature ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Visual Focus of Attention (VFOA) estimation in conversation is challenging as it relies on difficult to estimate information (gaze) combined with scene features like target positions and other contextual information (speaking status) allowing to disambigua ...

IEEE COMPUTER SOC2021

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

Jean-Marc Odobez, Michael Villamizar

We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions such as in state-of-the-art RNN-based approach ...

IEEE COMPUTER SOC2021

ManiGaze: a Dataset for Evaluating Remote Gaze Estimator in Object Manipulation Situations

Jean-Marc Odobez, Rémy Alain Siegfried, Bozorgmehr Aminian

Gaze estimation allows robots to better understand users and thus to more precisely meet their needs. In this paper, we are interested in gaze sensing for analyzing collaborative tasks and manipulation behaviors in human-robot interactions (HRI), which dif ...

ACM2020

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We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the part ...

2020

A Differential Approach for Gaze Estimation

Jean-Marc Odobez, Yu Yu, Gang Liu

Most non-invasive gaze estimation methods regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and t ...

2020