# Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

## TL;DR

This paper introduces a robust generative framework for depth-based 3D facial pose tracking that adapts in real-time to occlusions and expression changes, improving accuracy over previous methods.

## Contribution

It presents a novel statistical 3D morphable model with online adaptation and a ray visibility constraint to enhance robustness against occlusions.

## Key findings

- Outperforms state-of-the-art depth-based methods on Biwi and ICT-3DHP datasets.
- Effective in unconstrained scenarios with heavy occlusions and expression variations.
- Demonstrates the benefit of visibility constraints over ICP-based pose estimation.

## Abstract

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods.