I am a second year PhD student in Computer Vision at The University of York, UK. My research supervisors are Dr. William Smith and Prof. Edwin Hancock. My research topic is statistical 3D shape modelling, and I'm becoming interested in estimating human bodies shape and pose from different data modalities. I'm now a PhD Intern supervised by Dr. Gerard Pons-Moll, at Perceiving Systems.
In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Washington, DC, USA, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July 2017, Spotlight (inproceedings)
We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited statistical models of body shape produce overly smooth shapes lacking personalized details. In this paper we contribute a new approach to recover not only an approximate shape of the person, but also their detailed shape. Our approach allows the estimated shape to deviate from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available a new high quality 4D dataset that enables quantitative evaluation. Our method outperforms the previous state of the art, both qualitatively and quantitatively.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems