# Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections

arXiv: https://arxiv.org/abs/2206.08497 · HTML: https://mlsanity.com/paper/2206.08497/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [ARCH: Animatable Reconstruction of Clothed Humans](https://mlsanity.com/paper/2004.04572/) (2020)
- [Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies](https://mlsanity.com/paper/1801.01615/) (2018)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB](https://mlsanity.com/paper/1712.01057/) (2017)
- [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation](https://mlsanity.com/paper/2104.07645/) (2021)
- [Detailed Human Avatars from Monocular Video](https://mlsanity.com/paper/1808.01338/) (2018)
- [MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments](https://mlsanity.com/paper/1712.03931/) (2017)
- [Deep Part Induction from Articulated Object Pairs](https://mlsanity.com/paper/1809.07417/) (2018)
- [A Probabilistic Framework for Learning Kinematic Models of Articulated Objects](https://mlsanity.com/paper/1405.7705/) (2014)
- [CHALET: Cornell House Agent Learning Environment](https://mlsanity.com/paper/1801.07357/) (2018)
- [Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks](https://mlsanity.com/paper/1612.07429/) (2016)
- [RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud](https://mlsanity.com/paper/2006.14865/) (2020)
- [Learning Articulated Motions From Visual Demonstration](https://mlsanity.com/paper/1502.01659/) (2015)
- [StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects](https://mlsanity.com/paper/2105.08016/) (2021)
- [Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects](https://mlsanity.com/paper/2110.04411/) (2021)

## Built on by (most cited first, 13 of 13)

- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)
- [CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects](https://mlsanity.com/paper/2303.15782/) (2023)
- [PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation](https://mlsanity.com/paper/2312.03015/) (2023)
- [NAP: Neural 3D Articulation Prior](https://mlsanity.com/paper/2305.16315/) (2023)
- [S2O: Static to Openable Enhancement for Articulated 3D Objects](https://mlsanity.com/paper/2409.18896/) (2024)
- [FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion](https://mlsanity.com/paper/2510.25765/) (2025)
- [ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation](https://mlsanity.com/paper/2311.14262/) (2023)
- [Semi-Weakly Supervised Object Kinematic Motion Prediction](https://mlsanity.com/paper/2303.17774/) (2023)
- [LARM: A Large Articulated-Object Reconstruction Model](https://mlsanity.com/paper/2511.11563/) (2025)
- [ART: Articulated Reconstruction Transformer](https://mlsanity.com/paper/2512.14671/) (2025)
- [ArtHOI: Taming Foundation Models for Monocular 4D Reconstruction of Hand-Articulated-Object Interactions](https://mlsanity.com/paper/2603.25791/) (2026)
- [CA2T-Net: Category-Agnostic 3D Articulation Transfer from Single Image](https://mlsanity.com/paper/2301.02232/) (2023)
- [Mates2Motion: Learning How Mechanical CAD Assemblies Work](https://mlsanity.com/paper/2208.01779/) (2022)

## Abstract (arXiv)

3D models of manufactured objects are important for populating virtual worlds and for synthetic data generation for vision and robotics. To be most useful, such objects should be articulated: their parts should move when interacted with. While articulated object datasets exist, creating them is labor-intensive. Learning-based prediction of part motions can help, but all existing methods require annotated training data. In this paper, we present an unsupervised approach for discovering articulated motions in a part-segmented 3D shape collection. Our approach is based on a concept we call category closure: any valid articulation of an object's parts should keep the object in the same semantic category (e.g. a chair stays a chair). We operationalize this concept with an algorithm that optimizes a shape's part motion parameters such that it can transform into other shapes in the collection. We evaluate our approach by using it to re-discover part motions from the PartNet-Mobility dataset. For almost all shape categories, our method's predicted motion parameters have low error with respect to ground truth annotations, outperforming two supervised motion prediction methods.