AudioVisual Speech Synthesis: A brief literature review
Not on the map. Why?
Abstract (arXiv)
This brief literature review studies the problem of audiovisual speech synthesis, which is the problem of generating an animated talking head given a text as input. Due to the high complexity of this problem, we approach it as the composition of two problems. Specifically, that of Text-to-Speech (TTS) synthesis as well as the voice-driven talking head animation. For TTS, we present models that are used to map text to intermediate acoustic representations, e.g. mel-spectrograms, as well as models that generate voice signals conditioned on these intermediate representations, i.e vocoders. For the talking-head animation problem, we categorize approaches based on whether they produce human faces or anthropomorphic figures. An attempt is also made to discuss the importance of the choice of facial models in the second case. Throughout the review, we briefly describe the most important work in audiovisual speech synthesis, trying to highlight the advantages and disadvantages of the various approaches.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 66 references that are in the map. First posted on arXiv 18 Feb 2021.
Builds on
32 of 66 references.
References · 32
- 2017Attention Is All You Needcited by 59,932
- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 862
- 2016Conditional Image Generation with PixelCNN Decoderscited by 859
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
- 2020First Order Motion Model for Image Animationcited by 698
- 2017Masked Autoregressive Flow for Density Estimationcited by 680
- 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
- 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
20 more
- 2017How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)cited by 524
- 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
- 2013Do Deep Nets Really Need to be Deep?cited by 466
- 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 351
- 2020MakeItTalk: Speaker-Aware Talking-Head Animationcited by 310
- 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 308
- 20193D Morphable Face Models -- Past, Present and Futurecited by 269
- 2019Capture, Learning, and Synthesis of 3D Speaking Stylescited by 257
- 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
- 2017MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstructioncited by 163
- 2019Realistic Speech-Driven Facial Animation with GANscited by 142
- 2016SampleRNN: An Unconditional End-to-End Neural Audio Generation Modelcited by 110
- 2018LPCNet: Improving Neural Speech Synthesis Through Linear Predictioncited by 109
- 2019High Fidelity Speech Synthesis with Adversarial Networkscited by 90
- 2017Deep Voice 2: Multi-Speaker Neural Text-to-Speechcited by 75
- 2018ClariNet: Parallel Wave Generation in End-to-End Text-to-Speechcited by 70
- 2018VisemeNet: Audio-Driven Animator-Centric Speech Animationcited by 62
- 2018FloWaveNet : A Generative Flow for Raw Audiocited by 54
- 2019WaveFlow: A Compact Flow-based Model for Raw Audiocited by 51
- 2019Probability density distillation with generative adversarial networks for high-quality parallel waveform generationcited by 14
Built on by
Not cited yet.