Variational Autoencoders for Deforming 3D Mesh Models

3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-of-the-art methods.Comment: CVPR 201

Dynamic coupling modelling and application case analysis of high-slip motors and pumping units

To solve the issues and difficulties in the high-coupling modelling of beam pumping units and high-slip motors, external characteristic experiments of high-slip motors were performed where the external database and characteristic correlation equations of the motors were obtained through data regression analysis. Based on the analysis of the kinematics, dynamics and driving characteristics of the beam pumping unit, a fully coupled mathematical model of a motor, pumping unit, sucker rod and oil pump was established. The differential pumping equation system of the pumping unit used a cyclic iteration method to solve the problem of high coupling among the motor, pumping unit, sucker rod and the pumping pump. The model was verified by experimental data of field l pumping wells. Theoretical calculations and experimental tests showed that the soft characteristic of the high-slip motor can reduce the peak suspension load of the sucker rod, peak net torque of the gearbox and peak power of the motor. In addition, the results show that the soft characteristic can also decrease the high-frequency fluctuation of the motor power curve and the torque curve of the gearbox. The highslip motor can improve the smoothness and safety of the pumping well system

Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders

Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions at a similar scale while deformations of real-world objects are usually distributed in a multi-scale manner. In this paper, we propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder. The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales. Quantitative and qualitative evaluations show that our method outperforms state-of-the-art methods. Furthermore, with the multiscale deformation components extracted by our method, the user can edit shapes in a coarse-to-fine fashion which facilitates effective modeling of new shapes.Comment: 15 page