Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment

TL;DR

This paper introduces a simple, efficient method for dense 3D facial reconstruction from a single depth image using an affordable RGB-D sensor, enhancing virtual reality applications with higher accuracy and lower complexity.

Contribution

The paper proposes a novel approach that simplifies dense 3D facial reconstruction by using only one depth frame, reducing processing complexity and system cost.

Findings

Competitive results on real-world data

Effective reconstruction with a single depth image

Reduced processing complexity

Abstract

With the increasing demands of applications in virtual reality such as 3D films, virtual Human-Machine Interactions and virtual agents, the analysis of 3D human face analysis is considered to be more and more important as a fundamental step for those virtual reality tasks. Due to information provided by an additional dimension, 3D facial reconstruction enables aforementioned tasks to be achieved with higher accuracy than those based on 2D facial analysis. The denser the 3D facial model is, the more information it could provide. However, most existing dense 3D facial reconstruction methods require complicated processing and high system cost. To this end, this paper presents a novel method that simplifies the process of dense 3D facial reconstruction by employing only one frame of depth data obtained with an off-the-shelf RGB-D sensor. The experiments showed competitive results with real world data.