Research

I work on machine learning-guided in-betweening for hand-drawn animation. Since traditional 2D animation is time-consuming and expensive to produce, there is great interest in reducing animators’ workload, and one way to do this would be to reduce the work involved in drawing in-betweens. I am interested in using deep learning, computer vision-based methods to be able to understand the content of simple sequential pencil-on-paper drawings, and generate high-fidelity in-betweens where necessary. Currently we are exploring flow-based video interpolation methods, used in conjunction with a foreground segmentation module and an adversarial module to better adapt to a hand-drawn domain (“pencil tests”).

Other projects I have worked on include: