## 2 Related Work
### Three-Dimensional Face Reconstruction  
Three-dimensional face reconstruction is the task of reconstructing a human face from an image into a 3D form (or mesh). Over the last few decades, researchers have expended great efforts on this matter due to its challenges and its huge applicability. As a result, substantial progress has been made, which has led to novel and powerful algorithms that obtain impressive results, even in the very challenging case of reconstruction from a single RGB or RGB-D camera. According to [PERSON] et al. [16], unlike RGB cameras, RGB-D sensors capture both color and depth data at real-time rates. This helps to solve the inherent depth ambiguity of the monocular reconstruction problem, since a coarse geometry estimate is available at least. The use of an RGB-D also enables a more reliable and realistic reconstruction of the face. The approaches that work with RGB-D as an input with depth sensors can be classified as passive or active devices. Passive depth sensors are most commonly implemented via a stereo camera setup. If a point is found in both views of a calibrated stereo setup, the 3D point can be reconstructed through triangulation. Active cameras work with a light projector. These structured light cameras are widespread (e.g., Microsoft Kinect, Primesense Carmine, Intel Realsense) and provide relatively good depth in the near range, which is important for face-tracking tasks. Time-of-flight (ToF) cameras are another type of active depth camera. These RGB-D cameras compute depth by measuring the round-trip time of a light pulse (e.g., Creative Senz3D or Microsoft Kinect One). In the work proposed by [PERSON] et al. [17], one of the main contributions is a new real-time algorithm to reconstruct the high-quality facial performance of each actor in real-time from an RGB-D stream, captured in a general environment with large Lambertiansurfaces and smoothly varied lighting. The proposed method uses a parametric face model that spans a PCA space of facial identities, face poses and corresponding skin albedo. This model, which learns from real face scans, works as a statistical prior and an intermediate representation, which finally enables the photo-realistic re-rendering of the entire face. [PERSON] et al. [18] proposed an approach that unites facial tracking, segmentation and tracking model personalization from an RGB-D model. They detect dynamic occlusions caused by temporal shape and texture variations using an outlier voting scheme in superpixel space. The model demonstrates robust and high-fidelity facial tracking on a wide range of subjects with highly incomplete and largely occluded data. Another interesting work that uses RGB-D as input was proposed by [PERSON] et al. [19]. This work demonstrates that online model building can replace user-specific training and manual calibration for the facial performance of capture systems while maintaining high tracking accuracy. It simply requires a low-cost 3D sensor and no manual assistance of any kind. The authors introduced an adaptive dynamic expression model that, in turn, combines a dynamic expression template, an identity PCA model and a parameterized deformation model in a low-dimensional representation, which is suitable for online learning. One of the main problems with RGB-D cameras is the intrinsic noise that can affect the final result in face reconstruction. Filtering is a solution, but in some cases, it may not provide a satisfactory result. A face has some particular properties; thus, it is important that the filtering preserves them so as not to defeature the reconstruction. In this sense, we presented a filter approach based on exemplars [15]. In recent years, several filtering approaches have been proposed to process mesh geometry, denoising and smoothing meshes, targeting the output from 3D scanners or RGB-D cameras. The image processing literature considers that methods of mesh denoising may be classified as isotropic or anisotropic [20; 21].