His current project involves the development of new software to automatically label body position and motion in 3D space. Improvements to 'Marker Labelling' like this will save time and money for industries such as film and animation studios.
"Auto-labelling of Markers in Optical Motion Capture by Permutation Learning"
Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body, is a time consuming and labour-intensive postprocessing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework.
Saeed Ghorbani won Best Full Paper Award at the International Computer Graphics Conference 2019 and the 2nd Place Computer Vision Poster Award at the CVR-VISTA International Conference on Predictive Vision.