Supervised coordinate descent method with a 3D bilinear model for face alignment and tracking

Face alignment and tracking play important roles in facial performance capture. Existing data-driven methods for monocular videos suffer from large variations of pose and expression. In this paper, we propose an efficient and robust method for this task by introducing a novel supervised coordinate descent method with 3D bilinear representation. Instead of learning the mapping between the whole parameters and image features directly with a cascaded regression framework in current methods, we learn individual sets of parameters mappings separately step by step by a coordinate descent mean. Because different parameters make different contributions to the displacement of facial landmarks, our method is more discriminative to current whole-parameter cascaded regression methods. Benefiting from a 3D bilinear model learned from public databases, the proposed method can handle the head pose changes and extreme expressions out of plane better than other 2D-based methods. We present the reliable result of face tracking under various head poses and facial expressions on challenging video sequences collected online. The experimental results show that our method outperforms state-of-art data-driven methods

Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge

This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge. Our method builds upon the idea of convolutional part heatmap regression [1], extending it for 3D face alignment. Our method decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z (depth) estimation. At the first stage, our method estimates the X,Y coordinates of the facial landmarks by producing a set of 2D heatmaps, one for each landmark, using convolutional part heatmap regression. Then, these heatmaps, alongside the input RGB image, are used as input to a very deep subnetwork trained via residual learning for regressing the Z coordinate. Our method ranked 1st in the 3DFAW Challenge, surpassing the second best result by more than 22%.Comment: Winner of 3D Face Alignment in the Wild (3DFAW) Challenge, ECCV 201

3D facial performance capture from monocular RGB video.

3D facial performance capture is an essential technique for animation production in featured films, video gaming, human computer interaction, VR/AR asset creation and digital heritage, which all have huge impact on our daily life. Traditionally, dedicated hardware such as depth sensors, laser scanners and camera arrays have been developed to acquire depth information for such purpose. However, such sophisticated instruments can only be operated by trained professionals. In recent years, the wide spread availability of mobile devices, and the increased interest of casual untrained users in applications such as image, video editing, virtual and facial model creation, have sparked interest in 3D facial reconstruction from 2D RGB input. Due to the depth ambiguity and facial appearance variation, 3D facial performance capture and modelling from 2D images are inherently ill-posed problems. However, with strong prior knowledge of the human face, it is possible to accurately infer the true 3D facial shape and performance from multiple observations captured with different viewing angles. Various 3D from 2D methods have been proposed and proven to work well in controlled environments. Nevertheless there are still many unexplored issues in uncontrolled in-the-wild environments. In order to achieve the same level of performance in controlled environments, interfering factors in uncontrolled environments such as varying illumination, partial occlusion and facial variation not captured by prior knowledge would require the development of new techniques. This thesis addresses existing challenges and proposes novel methods involving 2D landmark detection, 3D facial reconstruction and 3D performance tracking, which are validated through theoretical research and experimental studies. 3D facial performance tracking is a multidisciplinary problem involving many areas such as computer vision, computer graphics and machine learning. To deal with the large variations within a single image, we present new machine learning techniques for facial landmark detection based on our observation of the facial features in challenging scenarios to increase the robustness. To take advantage of the evidence aggregated from multiple observations, we present new robust and efficient optimisation techniques that impose consistency constrains that help filter out outliers. To exploit the person-specific model generation, temporal and spatial coherence in continuous video input, we present new methods to improve the performance via optimisation. In order to track the 3D facial performance, the fundamental prerequisite for good results is the accurate underlying 3D model of the actor. In this thesis, we present new methods that are targeted at 3D facial geometry reconstruction, which are more efficient than existing generic 3D geometry reconstruction methods. Evaluation and validation were obtained and analysed from substantial experiment, which shows the proposed methods in this thesis outperform the state-of-the-art methods and enable us to generate high quality results with less constraints

Realtime Dynamic 3D Facial Reconstruction for Monocular Video In-the-Wild

With the increasing amount of videos recorded using 2D mobile cameras, the technique for recovering the 3D dynamic facial models from these monocular videos has become a necessity for many image and video editing applications. While methods based parametric 3D facial models can reconstruct the 3D shape in dynamic environment, large structural changes are ignored. Structure-from-motion methods can reconstruct these changes but assume the object to be static. To address this problem we present a novel method for realtime dynamic 3D facial tracking and reconstruction from videos captured in uncontrolled environments. Our method can track the deforming facial geometry and reconstruct external objects that protrude from the face such as glasses and hair. It also allows users to move around, perform facial expressions freely without degrading the reconstruction quality