Mathieu Marsot

Mathieu Marsot

Publications

Correspondence-free online human motion retargeting

Published in HAL, 2023

This paper presents a novel data-driven framework for unsupervised human motion retargeting which animates a target body shape with a source motion. This allows to retarget motions between different characters by animating a target subject with a motion of a source subject.

Recommended citation: Marsot, Mathieu and Rekik, Rim and Wuhrer, Stefanie and Franco, Jean-Sébastien and Olivier, Anne-Hélène. (2023). "Correspondence-free online human motion retargeting." HAL. 1(3). https://hal.inria.fr/hal-03970689v1/file/Motion_retargeting_CVPR_2023.pdf

Representing motion as a sequence of latent primitives, a flexible approach for human motion modelling

Published in HAL, 2022

This paper proposes a new representation of human body motion which encodes a full motion in a sequence of latent motion primitives.

Recommended citation: Marsot, Mathieu and Wuhrer, Stefanie and Franco, Jean-Sebastien and Olivier, Anne Hélène. (2022). "Representing motion as a sequence of latent primitives, a flexible approach for human motion modelling Number 2." HAL. 1(2). https://hal.science/hal-03715820v2

A Structured Latent Space for Human Body Motion Generation

Published in 2022 International Conference on 3D Vision (3DV), 2022

This paper proposes a framework to learn a structured latent space to represent 4D human body motion, where each latent vector encodes a full motion of the whole 3D human shape

Recommended citation: Marsot, Mathieu and Wuhrer, Stefanie and Franco, Jean-Sébastien and Durocher, Stephane. (2022). "A Structured Latent Space for Human Body Motion Generation." 2022 International Conference on 3D Vision (3DV). 1(1). https://hal.science/hal-03250297/file/A_structure_latent_space_for_human_body_motion_generation.pdf

An adaptive pig face recognition approach using Convolutional Neural Networks

Published in Computers and Electronics in Agriculture, 2020

In this paper, a novel framework composed of computer vision algorithms, machine learning and deep learning techniques is proposed to offer a relatively low-cost and scalable solution of pig recognition.

Recommended citation: Marsot, Mathieu and Mei, Jiangqiang and Shan, Xiaocai and Ye, Liyong and Feng, Peng and Yan, Xuejun and Li, Chenfan and Zhao, Yifan "An adaptive pig face recognition approach using Convolutional Neural Networks." Computers and Electronics in Agriculture. 1(1). https://www.sciencedirect.com/science/article/pii/S0168169920300673