mlsanityData to 7 Oct 2026

AutoLV: Automatic Lecture Video Generator

arXiv 2209.08795 ↗First posted 19 Sep 2022cs.MM cs.CV cs.SDcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 19 Sep 2022.

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23 of 34 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
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  12. 2018Efficient Neural Audio Synthesiscited by 218
11 more
  1. 2021A Survey on Neural Speech Synthesiscited by 182
  2. 2018Neural Voice Cloning with a Few Samplescited by 100
  3. 2017Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attentioncited by 57
  4. 2017Deep Speaker: an End-to-End Neural Speaker Embedding Systemcited by 56
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  6. 2017ObamaNet: Photo-realistic lip-sync from textcited by 42
  7. 2018Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesiscited by 30
  8. 2021Librispeech Transducer Model with Internal Language Model Prior Correctioncited by 26
  9. 2020BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimizationcited by 22
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  11. 2020AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIANcited by 14

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