6 research outputs found
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
A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint
3D shape editing is widely used in a range of applications such as movie
production, computer games and computer aided design. It is also a popular
research topic in computer graphics and computer vision. In past decades,
researchers have developed a series of editing methods to make the editing
process faster, more robust, and more reliable. Traditionally, the deformed
shape is determined by the optimal transformation and weights for an energy
term. With increasing availability of 3D shapes on the Internet, data-driven
methods were proposed to improve the editing results. More recently as the deep
neural networks became popular, many deep learning based editing methods have
been developed in this field, which is naturally data-driven. We mainly survey
recent research works from the geometric viewpoint to those emerging neural
deformation techniques and categorize them into organic shape editing methods
and man-made model editing methods. Both traditional methods and recent neural
network based methods are reviewed