# Unsupervised Shape and Pose Disentanglement for 3D Meshes

Source: [Unsupervised Shape and Pose Disentanglement for 3D Meshes](<https://www.sota2.com/research/paper/68fa680c1e209131cea19cf3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:28:00.017Z

Authors: Keyang Zhou, Bharat Lal Bhatnagar, Gerard Pons-Moll

Published: 2020-07-22T11:00:27.000Z

## Abstract

Parametric models of humans, faces, hands and animals have been widely used for a range of tasks such as image-based reconstruction, shape correspondence estimation, and animation. Their key strength is the ability to factor surface variations into shape and pose dependent components. Learning such models requires lots of expert knowledge and hand-defined object-specific constraints, making the learning approach unscalable to novel objects. In this paper, we present a simple yet effective approach to learn disentangled shape and pose representations in an unsupervised setting. We use a combination of self-consistency and cross-consistency constraints to learn pose and shape space from registered meshes. We additionally incorporate as-rigid-as-possible deformation(ARAP) into the training loop to avoid degenerate solutions. We demonstrate the usefulness of learned representations through a number of tasks including pose transfer and shape retrieval. The experiments on datasets of 3D humans, faces, hands and animals demonstrate the generality of our approach. Code is made available at https://virtualhumans.mpi-inf.mpg.de/unsup\_shape\_pose/.

## 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 Animal SMAL](<https://www.sota2.com/research/sota/unconditional-mesh-shape-generation-on-animal-smal.md>): Unconditional mesh shape generation; Animal SMAL; Error Rate (ER): 15.2.

- [Unconditional Mesh Shape Generation on Human](<https://www.sota2.com/research/sota/unconditional-mesh-shape-generation-on-human.md>): Unconditional mesh shape generation; Human; ER: 10.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.11341>)

[PDF](<https://arxiv.org/pdf/2007.11341.pdf>)