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Laboratoire/Entreprise : IRIMAS, Université Haute-Alsace
Durée : 6 months
Contact : maxime.devanne@uha.fr
Date limite de publication : 2024-12-23

Contexte :
Human motion analysis is crucial for studying people and understanding how they behave, communicate and interact with real world environments. Due to the complex nature of body movements as well as the high cost of motion capture systems, acquisition of human motion is not straightforward and thus constraints data production. Hopefully, recent approaches estimating human poses from videos offer new opportunities to analyze skeleton-based human motion. While skeleton-based human motion analysis has been extensively studied for behavior understanding like action recognition, some efforts are yet to be done for the task of human motion generation. Particularly, the automatic generation of motion sequences is beneficial for rapidly increasing the amount of data and improving Deep Learning-based analysis algorithms. In particular, this is crucial in a medical context like in physical rehabilitation where acquiring data is challenging. Rehabilitation human motions are corresponding to reha- bilitation exercises proposed by physiotherapists. Unlike classification tasks, the targeted task in human rehabilitation assessment is often a regression problem, where given a motion sequence, the goal is to predict the associated performance score given by physiotherapists.
Since several years, human motion generation paradigms have been possible thanks to the appearance of Generative Adversarial Networks (GAN), Vari- ational AutoEncoder (VAE) or Diffusion models. While most of these works have considered motion capture (mocap) data, we consider noisy skeleton data estimated from videos as it is easily applicable in real-world scenarios for the general public.

Sujet :
The goal of this internship is to investigate deep generative models for skeleton- based human motion sequences with a particular focus on rehabilitation data. Inspiring from recent effective Deep Learning-based approaches, the aim is to generate full skeleton-based rehabilitation motion sequences. It is therefore crucial to investigate how deep generative models can handle such noisy and possibly incomplete data in order to generate novel rehabilitation motion sequences as natural and variable as possible.
In particular, the candidate will work on the following tasks:
– Deep generative models adapted to rehabilitation data: based on studies from existing works, the goal is to build generative models for rehabilitation sequences. Therefore, the candidate will investigate different generative models, like Diffusion models, in order to propose and develop a complete Deep Learning model for generating skeleton-based human motions. These models will be trained using publicly available datasets such as the Kimore dataset.
– Evaluation of deep generative models: in order to validate the proposed model, experimental evaluation is crucial. In comparison to motion recognition where classification accuracy is a natural way to assess an approach, evaluating the task of motion generation is not as straightforward. Dedicated metrics evaluating both naturalness and diversity of generated sequences as well as the impact of new generated sequences in a classifi- cation task will be considered.
– Text to rehabilitation motion: The generated models will be then adapted to take as input text sequences corresponding to rehabilitation exercises’ descriptions. This will be particularly useful to create new rehabilitation exercises.

Profil du candidat :
The candidate must fit the following requirements:
– Registered in Master 2 or last year of Engineering School (or equivalent) in Computer Science
– Advanced skills in Python programming are mandatory
– Good skills in Machine Learning & Deep Learning using related libraries (scikit-learn, Tensorflow, Pytorch, etc.) are required
– Knowledge and/or a first experience in human motion analysis will be appreciated
– Knowledge and/or a first experience in Natural Language Processing to handle text-to-motion generation

Formation et compétences requises :
The candidate must fit the following requirements:
– Registered in Master 2 or last year of Engineering School (or equivalent) in Computer Science
– Advanced skills in Python programming are mandatory
– Good skills in Machine Learning & Deep Learning using related libraries (scikit-learn, Tensorflow, Pytorch, etc.) are required
– Knowledge and/or a first experience in human motion analysis will be appreciated
– Knowledge and/or a first experience in Natural Language Processing to handle text-to-motion generation

Adresse d’emploi :
Université Haute-Alsace
12 rue des Frères Lumière
68093 Mulhouse

Document attaché : 202410230753_internship_position_delegation_generation_2025.pdf