  The emergence of commercial tools for real-time performance-based 2D
animation has enabled 2D characters to appear on live broadcasts and streaming
platforms. A key requirement for live animation is fast and accurate lip sync
that allows characters to respond naturally to other actors or the audience
through the voice of a human performer. In this work, we present a deep
learning based interactive system that automatically generates live lip sync
for layered 2D characters using a Long Short Term Memory (LSTM) model. Our
system takes streaming audio as input and produces viseme sequences with less
than 200ms of latency (including processing time). Our contributions include
specific design decisions for our feature definition and LSTM configuration
that provide a small but useful amount of lookahead to produce accurate lip
sync. We also describe a data augmentation procedure that allows us to achieve
good results with a very small amount of hand-animated training data (13-20
minutes). Extensive human judgement experiments show that our results are
preferred over several competing methods, including those that only support
offline (non-live) processing. Video summary and supplementary results at
GitHub link: https://github.com/deepalianeja/CharacterLipSync2D
