Lumi`ereNet: Lecture Video Synthesis from Audio
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Abstract (arXiv)
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].
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2019.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1.
4 papers.
- 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
- 2021NWT: Towards natural audio-to-video generation with representation learningcited by 14
- 2023Towards the generation of synchronized and believable non-verbal facial behaviors of a talking virtual agentcited by 5
- 2021One Shot Audio to Animated Video Generationcited by 0
- 2023Towards the generation of synchronized and believable non-verbal facial behaviors of a talking virtual agent15 Sep 2023
- 2021NWT: Towards natural audio-to-video generation with representation learning8 Jun 2021
- 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward25 Feb 2021
- 2021One Shot Audio to Animated Video Generation19 Feb 2021