# Capture, Learning, and Synthesis of 3D Speaking Styles

## TL;DR

This paper introduces a new 4D facial dataset and a neural network model, VOCA, capable of realistic 3D facial animation driven by speech, applicable to unseen subjects and multiple languages.

## Contribution

The paper presents a novel 4D face dataset and a neural network model that animates 3D faces from speech, generalizing to new subjects and languages without retargeting.

## Key findings

- VOCA can animate unseen subjects realistically.
- The dataset enables training of models with diverse speaking styles.
- VOCA supports multilingual speech input.

## Abstract

Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input - even speech in languages other than English - and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.