[Paper Review] Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders

Jie Yang, Lin Gao+4 more|arXiv (Cornell University)|Dec 4, 2020

3D Shape Modeling and Analysis70 references6 citations

TL;DR

This paper proposes a novel stacked attention-based autoencoder framework to automatically extract multiscale, localized deformation components from 3D mesh deformation datasets. By using attention mechanisms to focus on active deformation regions across scales and stacking autoencoders to decompose deformations hierarchically, the method enables coarse-to-fine shape editing with superior reconstruction quality and reduced distortion compared to state-of-the-art approaches.

ABSTRACT

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.

Motivation & Objective

• To address the limitation of existing methods that extract deformation components only at a single scale or fail to capture hierarchical local deformations.
• To enable coarse-to-fine shape editing by automatically identifying semantically meaningful, scale-diverse deformation components.
• To improve shape reconstruction fidelity and reduce distortion in data-driven mesh editing by leveraging multiscale representation.
• To integrate attention mechanisms into deep autoencoder architectures for dynamic, region-specific focus on deformation patterns.
• To support perceptually consistent editing by isolating deformation behaviors at varying spatial scales.

Proposed method

• A stacked autoencoder architecture is used, where each subsequent autoencoder operates on the residual of the previous one, enabling hierarchical decomposition of deformations.
• An attention mechanism is introduced to learn soft weights that highlight active deformation regions in the mesh, guiding subsequent autoencoders to focus on specific sub-regions.
• The input to the network is a mesh-based representation using shape context features, which effectively encode both local and global deformation patterns.
• Sparse regularization is applied to the fully connected layers of the autoencoders to enforce localization of deformation components and prevent overfitting.
• The network is trained end-to-end with a reconstruction loss, ensuring that the extracted components can faithfully reconstruct the original mesh deformations.
• The attention masks are learned during training and used to condition the feature extraction of deeper autoencoders, enabling scale-aware decomposition.

Experimental results

Research questions

• RQ1 Can an attention-based deep autoencoder architecture effectively extract multiscale, localized deformation components from 3D mesh deformation datasets?
• RQ2 How does the integration of attention mechanisms improve the localization and semantic meaningfulness of extracted deformation components?
• RQ3 To what extent does the stacked autoencoder design enhance the decomposition of global and local deformations compared to single-scale or non-stacked approaches?
• RQ4 Can the extracted deformation components support intuitive, coarse-to-fine shape editing with reduced distortion?
• RQ5 How does the method generalize across diverse deformation types, such as facial expressions and full-body poses?

Key findings

• The proposed method outperforms state-of-the-art approaches in both quantitative metrics and qualitative visual evaluation, particularly in capturing fine-scale deformations like wrist twisting and tail bending.
• When editing complex actions such as wrist turning or tail twisting, only the proposed method successfully preserves local details without distorting the global shape.
• Ablation studies show that removing the attention mechanism leads to loss of multiscale structure, resulting in less localized and less meaningful deformation components.
• The method achieves better reconstruction quality with lower distortion, especially in cases involving multiple deformation scales, as demonstrated on the SCAPE and Horse datasets.
• The extracted components are semantically meaningful and correspond to perceptually distinct deformation behaviors, such as arm raising and wrist rotation.
• The framework enables effective coarse-to-fine editing, where users can first modify large-scale features and then refine local details using scale-specific components.