[visionlist] Jobs: PhD position on Deep View Synthesis for VR Video at University of Bath

[Christian Richardt]

 *Deep View Synthesis for Virtual Reality Video*

*Project Description*
The goal of this project is to capture and reconstruct the visual
appearance of dynamic real-world environments to enable more immersive
virtual reality video experiences.

State-of-the-art VR video approaches (e.g. Anderson et al., 2016) produce
stereoscopic 360° video, which comprises separate 360° videos for the left
and right eye (like 3D movies, but in 360°). The videos can, for example,
be viewed on YouTube using a VR headset such as Google Cardboard or
Daydream. Unfortunately, such videos only allow viewers to look in
different directions, but they do not respond to any head motion such as
moving left/right, forward/backwards or up/down. Truly immersive VR video,
on the other hand, requires ‘freedom of motion’ in six degrees-of-freedom
(‘6-DoF’), so that viewers see the correct views of an environment
regardless of where they are (3 DoF) and where they are looking (+3 DoF).

This project aims to develop novel-view synthesis techniques using deep
learning that are capable of producing high-quality, temporally-coherent,
time-varying VR video of dynamic real-world environments from one or more
standard or 360-degree video cameras. In particular, the goal is to
convincingly reconstruct the visual dynamics of the real world, such as
people and moving animals or plants, so that the reconstructed dynamic
geometry can provide the foundation for a novel video-based rendering
approach that synthesises visually plausible novel views with 6
degrees-of-freedom for the specific head position and orientation of a
viewer in VR. This experience will provide correct motion parallax and
depth perception to the viewer (like Luo et al., 2018) to ensure
unparalleled realism and immersion.

Candidates should normally have a very good undergraduate degree
(equivalent to First Class), or a Master’s degree in visual computing,
computer science, or a related discipline. A strong mathematical background
and strong previous programming experience, preferably in C++ and/or
Python, is required. Candidates must have a strong interest in visual
computing, and previous experience in computer vision, computer graphics,
deep learning and image processing is highly desirable.

For more general information on studying for a PhD in computer science at
Bath, see:
http://www.bath.ac.uk/science/graduate-school/research-programmes/phd-computer-science/

Applicants should hold, or expect to receive, a First Class or high Upper
Second Class UK Honours degree (or the equivalent qualification gained
outside the UK) in a relevant subject. A master’s level qualification would
also be advantageous.

Informal enquiries are welcome and should be directed to Dr Christian
Richardt, c.richardt at bath.ac.uk

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

Please ensure that your quote the supervisor’s name and project title in
the ‘Your research interests’ section.

More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/

Anticipated start date: 30 September 2019.

*Funding Notes*
Candidates may be considered for a University Research Studentship which
will cover UK/EU tuition fees, a training support fee of £1,000 per annum
and a tax-free maintenance allowance at the UKRI Doctoral Stipend rate
(£14,777 in 2018-19) for a period of up to 3.5 years.

Note: ONLY UK and EU applicants are eligible for this studentship;
unfortunately, applicants who are classed as Overseas for fee-paying
purposes are NOT eligible for funding.

*References*
R. Anderson, D. Gallup, J. T. Barron, J. Kontkanen, N. Snavely, C.
Hernandez, S. Agarwal and S. M. Seitz, “Jump: Virtual Reality Video”. *ACM
Transactions on Graphics (Proceedings of SIGGRAPH Asia 2016)*.

B. Luo, F. Xu, C. Richardt, J.-H. Yong, “Parallax360: Stereoscopic 360°
Scene Representation for Head-Motion Parallax”. *IEEE Transactions on
Visualization and Computer Graphics (IEEE VR 2018)*.

<http://cs.bath.ac.uk/~nc537/>Dr Christian Richardt <https://richardt.name/>
Dr Neill Campbell <http://cs.bath.ac.uk/~nc537/>
*Department of Computer Science, University of Bath*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://visionscience.com/pipermail/visionlist_visionscience.com/attachments/20181107/473ab7b2/attachment.html>