Dense 3D facial reconstruction from a single depth image in unconstrained environment

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 is considered to be more and more important as a fundamental step in these tasks. Due to information provided by the 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 proposed method is composed of two main stages: (a) the acquisition of the initial 3D facial point cloud with automatically 3D facial region cropping, and (b) the generating of the dense facial point cloud with RBF-based adaptive 3D point interpolation. Experiments reported in this paper demonstrate the competitive results with real-world data.