[Jobinfo] Fwd: [ieeevr-group] PhD position Inria (France): Realistic Pose-Motion Transfer

-------- Forwarded Message --------
Subject: 	[ieeevr-group] PhD position Inria (France): Realistic 
Pose-Motion Transfer
Date: 	Wed, 2 May 2018 15:27:39 +0200
From: 	Franck Multon <franck.multon@irisa.fr>
Reply-To: 	ieeevr-group+managers@uncc.edu
To: 	ieeevr-group@uncc.edu



Dear colleagues,

MimeTIC (team.inria.fr/mimetic/) and Morpheo (team.inria.fr/morpheo/) 
have a joined three-years PhD grant "Realistic Pose-Motion Transfer". 
Please forward this information to any student who would be interested 
in such an experience.

PhD could take place either in Rennes (MimeTIC) or Grenoble (Morpheo) in 
France.

Please contact me for more information if needed.

Best regards

Franck Multon

*Context: *This PhD is part of the AVATAR INRIA project, a collaborative 
project between several INRIA teams with the aim  to significantly 
advance the field of AVATAR modeling in particular by improving their 
realism. The PhD will be shared between the Mimetic team in Rennes, 
specialized in animation and the Morpheo team in Grenoble, specialized 
in moving shape capture.

*Job Description: *One of the objective of AVATAR is the ability to 
transfer the motion captured from a user to its avatar in a faithfull 
way. One of the main problems is that avatars do not perfectly look like 
the user (morphology variability) and may interact with a different 
environment (environment variability). Hence, a motion is in practice 
not limited to joint angles that model mainly the pose, and as provided 
by traditional mocap systems, but  involves contextual information, such 
as relation in-between body surfaces and with the environment. This is 
especially true with contacts between body parts that cannot be captured 
with joint angles only. In order to better model human pose, a set of 
works consider the “interaction mesh”[Ho10, Bernardin17], a graph 
structure that connects joint centers and can be used to preserve 
distances between these centers when transfering body poses to an 
avatar. Interaction graphs aims at capturing the contextual information 
linked to the motion. However, while better preserving the interaction 
between body parts, the interaction mesh is still unable to accurately 
capture and transfer body surface information. The purpose of this PhD 
is to investigate innovative solutions that consider shape surface 
information instead of pose only information.

A first direction we want  to explore is the extension of the 
interaction mesh to body surface information. This can be done through a 
graph connecting mesh vertices instead of joint centers, such as a 
tetrahedral graph. Several challenges must be faced for that. First, 
shape representations must be topologically consistent among subjects 
and poses in order to be able to implement an interaction graph 
structure that transfers information at the vertex level. Second, an 
approach must be designed to transfer body poses through such 
interaction graph structures. As for previous works with joint centers, 
this can be first experimented with an optimization framework that finds 
pose parameters subject to body constraints from the interaction graph.

A second direction in the longer run will be to investigate how learning 
could benefit to the process  explain before, i.e. replacing the 
optimization with a data driven approach when transferring a captured 
pose to an avatar. Existing human datasets, such as the caesar dataset, 
enable relationships between body poses to be learned over different 
subjects. While this has been already largely studied in the literature, 
our objective here will be to study such relationships in the context of 
an interaction structure such as the interaction mesh or the interaction 
graph proposed before, to better capture and simulate the contextual 
meaning of the motion.


Expertise of the PhD candidate

The candidate should have a strong expertise at least in one of the 
following domains: computer geometry, computer animation, machine 
learning, optimization.

He or she will have to code demos and prototype and should consequently 
have skills in programming (Matlab, C++). A knowledge computer graphics 
tools (such as Maya, Unity, or 3DS Max would be useful).

Candidates should send CV + letter to: fmulton@irisa.fr and 
edmond.boyer@inria.fr

[Bernardin2017] A Bernardin, L Hoyet, A Mucherino, D Gonçalves, F Multon 
(2017) Normalized Euclidean distance matrices for human motion 
retargeting. Proceedings of the Tenth International Conference on Motion 
in Games, 15

[Ho2010] ESL Ho, T Komura, CL Tai (2010) Spatial relationship preserving 
character motion adaptation. ACM Transactions on Graphics (TOG) 29 (4), 33



-- 
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MULTON Franck
Professor, University of Rennes 2

Leader of MimeTIC team (University of Rennes2, INRIA, ENS Rennes, University of Rennes1, CNRS)

M2S Research Unit
ENS Rennes
Campus de Ker lann, Avenue Robert Schuman, 35170 Bruz - FRANCE
web :http://www.m2slab.com
tel : +33 631646357
fax : +33 299847100
mail :Franck.Multon@univ-rennes2.fr  
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