# ICface: Interpretable and Controllable Face Reenactment Using GANs

## TL;DR

ICface is a versatile, interpretable face reenactment model using GANs that allows controllable manipulation of facial pose and expressions with high visual quality, enabling advanced editing tasks.

## Contribution

It introduces a two-stage, self-supervised neural network that uses interpretable control signals for face reenactment, allowing flexible mixing and editing of facial attributes.

## Key findings

- ICface outperforms state-of-the-art methods in visual quality.
- It offers versatile control over pose and expressions.
- The model enables easy post-production editing.

## Abstract

This paper presents a generic face animator that is able to control the pose and expressions of a given face image. The animation is driven by human interpretable control signals consisting of head pose angles and the Action Unit (AU) values. The control information can be obtained from multiple sources including external driving videos and manual controls. Due to the interpretable nature of the driving signal, one can easily mix the information between multiple sources (e.g. pose from one image and expression from another) and apply selective post-production editing. The proposed face animator is implemented as a two-stage neural network model that is learned in a self-supervised manner using a large video collection. The proposed Interpretable and Controllable face reenactment network (ICface) is compared to the state-of-the-art neural network-based face animation techniques in multiple tasks. The results indicate that ICface produces better visual quality while being more versatile than most of the comparison methods. The introduced model could provide a lightweight and easy to use tool for a multitude of advanced image and video editing tasks.