2021-03686 - PhD Position F/M Learning Non-rigid Surface Matching

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

About the research centre or Inria department

Grenoble Rhône-Alpes Research Center groups together a few less than 650 people in 37 research teams and 8 research support departments.

Staff is localized on 5 campuses in Grenoble and Lyon, in close collaboration with labs, research and higher education institutions in Grenoble and Lyon, but also with the economic players in these areas.

Present in the fields of software, high-performance computing, Internet of things, image and data, but also simulation in oceanography and biology, it participates at the best level of international scientific achievements and collaborations in both Europe and the rest of the world.



The Ph.D. will take place within the Morpheo research team at Inria Grenoble Rhône-Alpes. The team deals with the capture and analysis of dynamic scenes from multi-camera studios, and operates its own 68 camera acquisition platform and cluster, http://kinovis.inrialpes.fr

The PhD topic is on surface matching which is  the process of finding correspondences between shape surfaces. Of particular interest whithin the Morpheo context is the application of surface matching to 3D human reconstructions with different body poses and dynamic clothing as captured  along time in dynamic mesh sequences. The objective is to achieve 4D temporally coherent meshes across complex dynamic scenes, e.g., mesh connectivity does not vary from frame-to-frame. The PhD will investigate learning based strategies for that purpose. 




The focus of this Ph.D. is on matching unstructured surfaces from human performance capture systems, improving on traditional approaches  using novel sophisticated learning-based techniques. Learning-based techniques appear with considerable interest in shape analyses and representation. Although most recent methods rely on statistical body models, which can lack on realistic surface deformations, in particular on clothing deformations. The direction of this research is towards enabling deep learning methods to efficiently represent non-rigid features, such as loose clothing and hair motion, consequently, facilitating matching of complex dynamic performance capture content.


Main activities

The purpose of this Ph.D. is therefore to investigate innovative solutions on mesh matching that allow representation of non-rigid dynamic mesh sequences leverage by recent deep learning strategies that can take benefit of existing body shape datasets to learn non-rigid mesh correspondences.


Technical skills and level required :

  • Solid Background in 3D Vision, Machine Learning and knowledge in Computer Animation. 
  • Solid programming skills, e.g. C++ and python.
  • Solid mathematical knowledge in linear algebra and statistics.

Languages : English mandatory, French optional.


Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (90 days / year) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage


1st and 2nd year: 1 982 euros gross salary /month

3rd year: 2 085 euros gross salary / month