# Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds

arXiv: https://arxiv.org/abs/2306.00979 · HTML: https://mlsanity.com/paper/2306.00979/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 69 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 30 of 36)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [Embodied Hands: Modeling and Capturing Hands and Bodies Together](https://mlsanity.com/paper/2201.02610/) (2022)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences](https://mlsanity.com/paper/1910.09165/) (2019)
- [A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation](https://mlsanity.com/paper/2104.07645/) (2021)
- [BAE-NET: Branched Autoencoder for Shape Co-Segmentation](https://mlsanity.com/paper/1903.11228/) (2019)
- [CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds](https://mlsanity.com/paper/2104.03437/) (2021)
- [Computing the Stereo Matching Cost with a Convolutional Neural Network](https://mlsanity.com/paper/1409.4326/) (2014)
- [ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory](https://mlsanity.com/paper/2008.10518/) (2020)
- [Deep Part Induction from Articulated Object Pairs](https://mlsanity.com/paper/1809.07417/) (2018)
- [OPD: Single-view 3D Openable Part Detection](https://mlsanity.com/paper/2203.16421/) (2022)
- [Self-supervised Neural Articulated Shape and Appearance Models](https://mlsanity.com/paper/2205.08525/) (2022)
- [Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects](https://mlsanity.com/paper/2112.11347/) (2021)
- [MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization](https://mlsanity.com/paper/2101.06605/) (2021)
- [Visual Identification of Articulated Object Parts](https://mlsanity.com/paper/2012.00284/) (2020)
- [Understanding 3D Object Articulation in Internet Videos](https://mlsanity.com/paper/2203.16531/) (2022)
- [CASA: Category-agnostic Skeletal Animal Reconstruction](https://mlsanity.com/paper/2211.03568/) (2022)
- [RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud](https://mlsanity.com/paper/2006.14865/) (2020)
- [Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation](https://mlsanity.com/paper/2012.00088/) (2020)
- [Factored Pose Estimation of Articulated Objects using Efficient Nonparametric Belief Propagation](https://mlsanity.com/paper/1812.03647/) (2018)
- [Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects](https://mlsanity.com/paper/2110.04411/) (2021)

## Built on by (most cited first, 30 of 34)

- [Real2Code: Reconstruct Articulated Objects via Code Generation](https://mlsanity.com/paper/2406.08474/) (2024)
- [SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects](https://mlsanity.com/paper/2410.16499/) (2024)
- [ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting](https://mlsanity.com/paper/2502.19459/) (2025)
- [CAGE: Controllable Articulation GEneration](https://mlsanity.com/paper/2312.09570/) (2023)
- [MeshArt: Generating Articulated Meshes with Structure-Guided Transformers](https://mlsanity.com/paper/2412.11596/) (2024)
- [DRAWER: Digital Reconstruction and Articulation With Environment Realism](https://mlsanity.com/paper/2504.15278/) (2025)
- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion](https://mlsanity.com/paper/2510.25765/) (2025)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [Survey on Modeling of Human-made Articulated Objects](https://mlsanity.com/paper/2403.14937/) (2024)
- [RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos](https://mlsanity.com/paper/2503.16822/) (2025)
- [SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting](https://mlsanity.com/paper/2506.03594/) (2025)
- [LARM: A Large Articulated-Object Reconstruction Model](https://mlsanity.com/paper/2511.11563/) (2025)
- [Category-Agnostic Neural Object Rigging](https://mlsanity.com/paper/2505.20283/) (2025)
- [Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects](https://mlsanity.com/paper/2511.01294/) (2025)
- [Predict-Optimize-Distill: A Self-Improving Cycle for 4D Object Understanding](https://mlsanity.com/paper/2504.17441/) (2025)
- [AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration](https://mlsanity.com/paper/2412.05507/) (2024)
- [Part Segmentation and Motion Estimation for Articulated Objects with Dynamic 3D Gaussians](https://mlsanity.com/paper/2506.22718/) (2025)
- [ArtHOI: Taming Foundation Models for Monocular 4D Reconstruction of Hand-Articulated-Object Interactions](https://mlsanity.com/paper/2603.25791/) (2026)
- [Demeter: A Parametric Model of Crop Plant Morphology from the Real World](https://mlsanity.com/paper/2510.16377/) (2025)
- [FunHOI: Annotation-Free 3D Hand-Object Interaction Generation via Functional Text Guidance](https://mlsanity.com/paper/2502.20805/) (2025)
- [DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction](https://mlsanity.com/paper/2509.12430/) (2025)
- [sim2art: Accurate Articulated Object Modeling from a Single Video using Synthetic Training Data Only](https://mlsanity.com/paper/2512.07698/) (2025)
- [PAOLI: Pose-free Articulated Object Learning from Sparse-view Images](https://mlsanity.com/paper/2509.04276/) (2025)
- [ArticFlow: Generative Simulation of Articulated Mechanisms](https://mlsanity.com/paper/2511.17883/) (2025)
- [SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting](https://mlsanity.com/paper/2601.00285/) (2026)
- [ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals](https://mlsanity.com/paper/2602.22666/) (2026)
- [GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting](https://mlsanity.com/paper/2604.07728/) (2026)
- [Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video](https://mlsanity.com/paper/2605.18645/) (2026)
- [AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos](https://mlsanity.com/paper/2609.24487/) (2026)

## Abstract (arXiv)

We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1 degree-of-freedom joints. Given point cloud videos of such everyday objects, our method identifies the distinct object parts, what parts are connected to what other parts, and the properties of the joints connecting each part pair. We do this by jointly optimizing the part segmentation, transformation, and kinematics using a novel energy minimization framework. Our inferred animatable models, enables retargeting to novel poses with sparse point correspondences guidance. We test our method on a new articulating robot dataset, and the Sapiens dataset with common daily objects, as well as real-world scans. Experiments show that our method outperforms two leading prior works on various metrics.