# ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators Source: [ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:10:35.496Z Authors: Qixing Huang, Xiangru Huang, Bo Sun, Zaiwei Zhang, Junfeng Jiang, Chandrajit Bajaj Published: 2021-08-21T04:22:21.000Z ## Abstract This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy. We show how to develop the unsupervised loss via a spectral decomposition of the Hessian of the ARAP energy. Our loss nicely decouples pose and shape variations through a robust norm. The loss admits simple closed-form expressions. It is easy to train and can be plugged into any standard generation models, e.g., variational auto-encoder (VAE) and auto-decoder (AD). Experimental results show that our approach outperforms existing shape generation approaches considerably on public benchmark datasets of various shape categories such as human, animal and bone. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Unconditional Mesh Shape Generation on Human](): Unconditional mesh shape generation; Human; ER: 4.52. - [Unconditional mesh shape generation on Animal SMAL](): Unconditional mesh shape generation; Animal SMAL; Error Rate (ER): 9.68. ## Original sources and implementation [Original paper]() [PDF]()