# Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation

arXiv: https://arxiv.org/abs/2104.07995 · HTML: https://mlsanity.com/paper/2104.07995/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 65 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [MakeItTalk: Speaker-Aware Talking-Head Animation](https://mlsanity.com/paper/2004.12992/) (2020)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [Deep Video Portraits](https://mlsanity.com/paper/1805.11714/) (2018)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [Neural Voice Puppetry: Audio-driven Facial Reenactment](https://mlsanity.com/paper/1912.05566/) (2019)
- [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://mlsanity.com/paper/1807.07860/) (2018)
- [X2Face: A network for controlling face generation by using images, audio, and pose codes](https://mlsanity.com/paper/1807.10550/) (2018)
- [Lip Movements Generation at a Glance](https://mlsanity.com/paper/1803.10404/) (2018)
- [Talking-head Generation with Rhythmic Head Motion](https://mlsanity.com/paper/2007.08547/) (2020)
- [Text-based Editing of Talking-head Video](https://mlsanity.com/paper/1906.01524/) (2019)
- [MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets](https://mlsanity.com/paper/1911.08139/) (2019)
- [ReenactGAN: Learning to Reenact Faces via Boundary Transfer](https://mlsanity.com/paper/1807.11079/) (2018)
- [Neural Style-Preserving Visual Dubbing](https://mlsanity.com/paper/1909.02518/) (2019)
- [Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose](https://mlsanity.com/paper/2003.12957/) (2020)
- [Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations](https://mlsanity.com/paper/1804.02812/) (2018)

## Built on by (most cited first, 30 of 47)

- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (2022)
- [Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion](https://mlsanity.com/paper/2107.09293/) (2021)
- [EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling](https://mlsanity.com/paper/2401.00374/) (2023)
- [StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles](https://mlsanity.com/paper/2301.01081/) (2023)
- [One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning](https://mlsanity.com/paper/2112.02749/) (2021)
- [Responsive Listening Head Generation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2112.13548/) (2021)
- [Diff2Lip: Audio Conditioned Diffusion Models for Lip-Synchronization](https://mlsanity.com/paper/2308.09716/) (2023)
- [Learning in Audio-visual Context: A Review, Analysis, and New Perspective](https://mlsanity.com/paper/2208.09579/) (2022)
- [Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers](https://mlsanity.com/paper/2212.04970/) (2022)
- [TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles](https://mlsanity.com/paper/2304.00334/) (2023)
- [FT2TF: First-Person Statement Text-To-Talking Face Generation](https://mlsanity.com/paper/2312.05430/) (2023)
- [Say Anything with Any Style](https://mlsanity.com/paper/2403.06363/) (2024)
- [DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation](https://mlsanity.com/paper/2312.13578/) (2023)
- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [Memories are One-to-Many Mapping Alleviators in Talking Face Generation](https://mlsanity.com/paper/2212.05005/) (2022)
- [Deep Learning for Visual Speech Analysis: A Survey](https://mlsanity.com/paper/2205.10839/) (2022)
- [Audio-Visual Face Reenactment](https://mlsanity.com/paper/2210.02755/) (2022)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.09767/) (2023)
- [ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer](https://mlsanity.com/paper/2408.03284/) (2024)
- [StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads](https://mlsanity.com/paper/2409.09292/) (2024)
- [Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text](https://mlsanity.com/paper/2106.14014/) (2021)
- [Conditional Temporal Variational AutoEncoder for Action Video Prediction](https://mlsanity.com/paper/2108.05658/) (2021)
- [Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator](https://mlsanity.com/paper/2305.02594/) (2023)
- [From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications](https://mlsanity.com/paper/2308.16041/) (2023)
- [DH-FaceVid-1K: A Large-Scale High-Quality Dataset for Face Video Generation](https://mlsanity.com/paper/2410.07151/) (2024)
- [AV-Flow: Transforming Text to Audio-Visual Human-like Interactions](https://mlsanity.com/paper/2502.13133/) (2025)
- [UniFLG: Unified Facial Landmark Generator from Text or Speech](https://mlsanity.com/paper/2302.14337/) (2023)
- [Text-to-Video: a Two-stage Framework for Zero-shot Identity-agnostic Talking-head Generation](https://mlsanity.com/paper/2308.06457/) (2023)
- [Audio-Driven 3D Facial Animation from In-the-Wild Videos](https://mlsanity.com/paper/2306.11541/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage and a speaker-specific stage. In the speaker-independent stage, we design three parallel networks to generate animation parameters of the mouth, upper face, and head from texts, separately. In the speaker-specific stage, we present a 3D face model guided attention network to synthesize videos tailored for different individuals. It takes the animation parameters as input and exploits an attention mask to manipulate facial expression changes for the input individuals. Furthermore, to better establish authentic correspondences between visual motions (i.e., facial expression changes and head movements) and audios, we leverage a high-accuracy motion capture dataset instead of relying on long videos of specific individuals. After attaining the visual and audio correspondences, we can effectively train our network in an end-to-end fashion. Extensive experiments on qualitative and quantitative results demonstrate that our algorithm achieves high-quality photo-realistic talking-head videos including various facial expressions and head motions according to speech rhythms and outperforms the state-of-the-art.