  We propose an end-to-end lecture video generation system that can generate
realistic and complete lecture videos directly from annotated slides,
instructor's reference voice and instructor's reference portrait video. Our
system is primarily composed of a speech synthesis module with few-shot speaker
adaptation and an adversarial learning-based talking-head generation module. It
is capable of not only reducing instructors' workload but also changing the
language and accent which can help the students follow the lecture more easily
and enable a wider dissemination of lecture contents. Our experimental results
show that the proposed model outperforms other current approaches in terms of
authenticity, naturalness and accuracy. Here is a video demonstration of how
our system works, and the outcomes of the evaluation and comparison:
https://youtu.be/cY6TYkI0cog.
