# On the role of Lip Articulation in Visual Speech Perception

arXiv: https://arxiv.org/abs/2203.10117 · HTML: https://mlsanity.com/paper/2203.10117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [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)
- [X2Face: A network for controlling face generation by using images, audio, and pose codes](https://mlsanity.com/paper/1807.10550/) (2018)
- [Audio-Driven Emotional Video Portraits](https://mlsanity.com/paper/2104.07452/) (2021)
- [Realistic Speech-Driven Facial Animation with GANs](https://mlsanity.com/paper/1906.06337/) (2019)
- [What comprises a good talking-head video generation?: A Survey and Benchmark](https://mlsanity.com/paper/2005.03201/) (2020)
- [Modality Dropout for Improved Performance-driven Talking Faces](https://mlsanity.com/paper/2005.13616/) (2020)
- [Generating Talking Face Landmarks from Speech](https://mlsanity.com/paper/1803.09803/) (2018)
- [Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models](https://mlsanity.com/paper/1905.06860/) (2019)
- [Audiovisual Speech Synthesis using Tacotron2](https://mlsanity.com/paper/2008.00620/) (2020)

## Built on by (most cited first, 1 of 1)

- [InfoSyncNet: Information Synchronization Temporal Convolutional Network for Visual Speech Recognition](https://mlsanity.com/paper/2508.02460/) (2025)

## Abstract (arXiv)

Generating realistic lip motion from audio to simulate speech production is critical for driving natural character animation. Previous research has shown that traditional metrics used to optimize and assess models for generating lip motion from speech are not a good indicator of subjective opinion of animation quality. Devising metrics that align with subjective opinion first requires understanding what impacts human perception of quality. In this work, we focus on the degree of articulation and run a series of experiments to study how articulation strength impacts human perception of lip motion accompanying speech. Specifically, we study how increasing under-articulated (dampened) and over-articulated (exaggerated) lip motion affects human perception of quality. We examine the impact of articulation strength on human perception when considering only lip motion, where viewers are presented with talking faces represented by landmarks, and in the context of embodied characters, where viewers are presented with photo-realistic videos. Our results show that viewers prefer over-articulated lip motion consistently more than under-articulated lip motion and that this preference generalizes across different speakers and embodiments.