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Capturing, Modelling and Learning Animal Facial Movement

  • Full or part time
  • Application Deadline
    Monday, April 01, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Human facial motion capture research is a well-established field with wide applications in digital film making to drive avatars. However, for some productions the driven digital characters may be animal (e.g. the recent Disney’s Jungle Book film) making direct re-targeting of human facial dynamics appear unrealistic. Animal expression re-creation in these cases is entirely dependent on the artist’s skill as animal facial motion capture remains an unexplored field. This PhD project is intended to pioneer research in this novel direction of animal facial dynamics modelling. Apart from facilitating the resource-saving automation in realistic animal facial performance content generation, the work will be solving a challenging theoretical problem of interest to the scientific community.

There have been early research efforts in dog facial landmark detection [1] akin the corresponding well-established work in human faces [2]. Although an important start, the sparsity of the detected facial landmark set for dogs makes it of limited usefulness in animation. Denser processing is therefore required to model dynamics. This may involve tracking a more complete set of keypoints for a blendshape model solve or 3D statistical modelling (e.g. 3DMM [3]) with the parameters of such a model subsequently regressed for new input by a trained neural network [4]. The statistical modelling task is complicated by the highly variable facial appearance within some animal species (e.g. different dog breeds). The research will hence involve development of generic algorithms for modelling in highly variable statistical spaces. The work will cover a wide range of activities from the use of multi-model data for 3D reconstruction, further data acquisition and the use of machine learning and optimisation techniques for modelling.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed to Prof Darren Cosker: .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

References

[1] Vlachynská, Alžběta & Kominkova Oplatkova, Zuzana & Turecek, Tomas. (2019). Dogface Detection and Localization of Dogface’s Landmarks. Artificial Intelligence and Algorithms in Intelligent Systems, pp.465-476

[2] Wu and Ji, Facial Landmark Detection: A Literature Survey, IJCV 2018

[3] J. Booth, A. Roussos, S. Zafeiriou, A. Ponniahy and D. Dunaway, "A 3D Morphable Model Learnt from 10,000 Faces," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016

[4] Tran et.al, Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network, CVPR 2017.

How good is research at University of Bath in Computer Science and Informatics?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

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