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

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

Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning

Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose haze degrees are different and enrich the underlying scene prior. Secondly, beyond utilizing the local neighboring relationship, we build a bidimensional graph reasoning module to conduct non-local filtering in the spatial and channel dimensions of feature maps, which models their long-range dependency and propagates the natural scene prior between the well-preserved nodes and the nodes contaminated by haze. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks

Fuzzy quaternion approach to object recognition incorporating Zernike moment invariants

Proceedings - International Conference on Pattern Recognition1288-290PICR

3D mesh simplification for deformable human body mesh using deformation saliency

3D mesh of human body is the foundation of many hot research topics, such as 3D body pose tracking. In this topic, the deformation of the human body mesh has to be taken into account because of various poses of the human body. Considering the time cost of the body deformation, however, it’s impractical to adopt a high resolution body mesh generated from scanning systems for the real-time tracking. Mesh simplification is a solution to reduce the size of body meshes and accelerate the deformation process. In this paper, we propose a mesh simplification algorithm using deformation saliency for such deformable human body meshes. This algorithm is based on quadric edge contraction. The deformation saliency is computed from a set of meshes with various poses. With this saliency, our algorithm can simplify the 3D mesh non-uniformly. Experiment shows that using our algorithm can improve the accuracy of body pose simulation in the simplified resolution compared to using classical quadric edge contraction methods