# DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction

arXiv: https://arxiv.org/abs/2509.12430 · HTML: https://mlsanity.com/paper/2509.12430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 28 of 28)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://mlsanity.com/paper/2308.07391/) (2023)
- [FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects](https://mlsanity.com/paper/2205.04382/) (2022)
- [CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds](https://mlsanity.com/paper/2104.03437/) (2021)
- [SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects](https://mlsanity.com/paper/2410.16499/) (2024)
- [Deep Part Induction from Articulated Object Pairs](https://mlsanity.com/paper/1809.07417/) (2018)
- [CAGE: Controllable Articulation GEneration](https://mlsanity.com/paper/2312.09570/) (2023)
- [Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling](https://mlsanity.com/paper/2502.02590/) (2025)
- [Towards Real-World Category-level Articulation Pose Estimation](https://mlsanity.com/paper/2105.03260/) (2021)
- [OPDMulti: Openable Part Detection for Multiple Objects](https://mlsanity.com/paper/2303.14087/) (2023)
- [Understanding 3D Object Articulation in Internet Videos](https://mlsanity.com/paper/2203.16531/) (2022)
- [The RBO Dataset of Articulated Objects and Interactions](https://mlsanity.com/paper/1806.06465/) (2018)
- [Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds](https://mlsanity.com/paper/2306.00979/) (2023)
- [DragAPart: Learning a Part-Level Motion Prior for Articulated Objects](https://mlsanity.com/paper/2403.15382/) (2024)
- [Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance](https://mlsanity.com/paper/2302.14268/) (2023)
- [S2O: Static to Openable Enhancement for Articulated 3D Objects](https://mlsanity.com/paper/2409.18896/) (2024)
- [Self-Supervised Learning of Part Mobility from Point Cloud Sequence](https://mlsanity.com/paper/2010.11735/) (2020)
- [Semi-Weakly Supervised Object Kinematic Motion Prediction](https://mlsanity.com/paper/2303.17774/) (2023)
- [Survey on Modeling of Human-made Articulated Objects](https://mlsanity.com/paper/2403.14937/) (2024)
- [CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer](https://mlsanity.com/paper/2402.17360/) (2024)
- [Learning to Infer Kinematic Hierarchies for Novel Object Instances](https://mlsanity.com/paper/2110.07911/) (2021)
- [Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images](https://mlsanity.com/paper/2303.11530/) (2023)
- [Opening Articulated Structures in the Real World](https://mlsanity.com/paper/2402.17767/) (2024)

## Built on by (most cited first, 2 of 2)

- [AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects](https://mlsanity.com/paper/2605.12845/) (2026)
- [MechVerse: Evaluating Physical Motion Consistency in Video Generation Models](https://mlsanity.com/paper/2605.14843/) (2026)

## Abstract (arXiv)

Understanding the motion of articulated mechanical assemblies from static geometry remains a core challenge in 3D perception and design automation. Prior work on everyday articulated objects such as doors and laptops typically assumes simplified kinematic structures or relies on joint annotations. However, in mechanical assemblies like gears, motion arises from geometric coupling, through meshing teeth or aligned axes, making it difficult for existing methods to reason about relational motion from geometry alone. To address this gap, we introduce MechBench, a benchmark dataset of 693 diverse synthetic gear assemblies with part-wise ground-truth motion trajectories. MechBench provides a structured setting to study coupled motion, where part dynamics are induced by contact and transmission rather than predefined joints. Building on this, we propose DYNAMO, a dependency-aware neural model that predicts per-part SE(3) motion trajectories directly from segmented CAD point clouds. Experiments show that DYNAMO outperforms strong baselines, achieving accurate and temporally consistent predictions across varied gear configurations. Together, MechBench and DYNAMO establish a novel systematic framework for data-driven learning of coupled mechanical motion in CAD assemblies.