In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process. This approximation performs well when the distance between camera and face is far enough. However, in some scenarios that the face is very close to camera or moving along the camera axis, the methods suffer from the inaccurate reconstruction and unstable temporal fitting due to the distortion under the perspective projection. In this paper, we aim to address the problem of single-image 3D face reconstruction under perspective projection. Specifically, a deep neural network, Perspective Network (PerspNet), is proposed to simultaneously reconstruct 3D face shape in canonical space and learn the correspondence between 2D pixels and 3D points, by which the 6DoF (6 Degrees of Freedom) face pose can be estimated to represent perspective projection. Besides, we contribute a large ARKitFace dataset to enable the training and evaluation of 3D face reconstruction solutions under the scenarios of perspective projection, which has 902,724 2D facial images with ground-truth 3D face mesh and annotated 6DoF pose parameters. Experimental results show that our approach outperforms current state-of-the-art methods by a significant margin. The code and data are available at https://github.com/cbsropenproject/6dof_face.

Abstract We present a realistic, fast, and controllable model to simulate fire propagation on solid objects with the object decomposition process involved. A hybrid structure of grids is employed to simulate the whole process efficiently. An improved burning surface update scheme based on level set and a novel method for visualizing the burning surface are proposed to produce convincing results. To achieve interactive simulation speed, a few acceleration techniques are employed, including a moving grid generated to dynamically track the fire propagation, a refined Marching Cubes method to reconstruct the burning surface, and a hardware‐implemented fluid solver. By controlling a few physical and geometric parameters, we are able to simulate various solid combustion phenomena. Copyright © 2011 John Wiley & Sons, Ltd.

We present a particle-based method for viscoelastic fluids simulation. In the method, based on the traditional Navier-Stokes equation, an additional elastic stress term is introduced to achieve viscoelastic flow behaviors, which have both fluid and solid features. Benefiting from the Lagrangian nature of Smoothed Particle Hydrodynamics, large flow deformation can be handled more easily and naturally. And also, by changing the viscosity and elastic stress coefficient of the particles according to the temperature variation, the melting and flowing phenomena, such as lava flow and wax melting, are achieved. The temperature evolution is determined with the heat diffusion equation. The method is effective and efficient, and has good controllability. Different kinds of viscoelastic fluid behaviors can be obtained easily by adjusting the very few experimental parameters.