English

Lumi\`ereNet: Lecture Video Synthesis from Audio

Machine Learning 2019-07-05 v1 Computer Vision and Pattern Recognition Audio and Speech Processing Machine Learning

Abstract

We present Lumi\`ereNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, Lumi\`ereNet is entirely composed of trainable neural network modules to learn mapping functions from the audio to video through (intermediate) estimated pose-based compact and abstract latent codes. Our video demos are available at [22] and [23].

Keywords

Cite

@article{arxiv.1907.02253,
  title  = {Lumi\`ereNet: Lecture Video Synthesis from Audio},
  author = {Byung-Hak Kim and Varun Ganapathi},
  journal= {arXiv preprint arXiv:1907.02253},
  year   = {2019}
}
R2 v1 2026-06-23T10:11:59.288Z