# FML: Face Model Learning from Videos

## TL;DR

This paper introduces a self-supervised deep learning approach that leverages in-the-wild videos to learn a general 3D face model, improving face reconstruction by ensuring multi-frame consistency.

## Contribution

It presents a novel multi-frame consistency loss and a self-supervised training method using internet videos, enabling robust 3D face reconstruction without limited 3D scans.

## Key findings

- Achieves high-quality 3D face reconstruction from videos.
- Learns a highly generalizable face model from in-the-wild data.
- Supports both monocular and multi-frame reconstruction.

## Abstract

Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existing methods rely on data-driven priors that are built from limited 3D face scans. In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces. Our face model is learned using only corpora of in-the-wild video clips collected from the Internet. This virtually endless source of training data enables learning of a highly general 3D face model. In order to achieve this, we propose a novel multi-frame consistency loss that ensures consistent shape and appearance across multiple frames of a subject's face, thus minimizing depth ambiguity. At test time we can use an arbitrary number of frames, so that we can perform both monocular as well as multi-frame reconstruction.