# GEOPARD: Geometric Pretraining for Articulation Prediction in 3D Shapes

arXiv: https://arxiv.org/abs/2504.02747 · HTML: https://mlsanity.com/paper/2504.02747/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2025.

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

- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos](https://mlsanity.com/paper/2604.05621/) (2026)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects](https://mlsanity.com/paper/2605.24403/) (2026)
- [Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints](https://mlsanity.com/paper/2603.11606/) (2026)
- [Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects](https://mlsanity.com/paper/2604.25781/) (2026)
- [Adaptive Volumetric Mechanical Property Fields Invariant to Resolution](https://mlsanity.com/paper/2606.18231/) (2026)
- [ArtHOI: Articulated Human-Object Interaction Synthesis by 4D Reconstruction from Video Priors](https://mlsanity.com/paper/2603.04338/) (2026)
- [ArtLang: Structured Language-to-Kinematics Grounding for Articulated 3D Actuation](https://mlsanity.com/paper/2608.15419/) (2026)
- [BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives](https://mlsanity.com/paper/2608.31113/) (2026)
- [FunArt: Decoding Functional Structure and Articulation from Generative 3D Latents](https://mlsanity.com/paper/2609.20673/) (2026)
- [ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image](https://mlsanity.com/paper/2610.05249/) (2026)

## Abstract (arXiv)

We present GEOPARD, a transformer-based architecture for predicting articulation from a single static snapshot of a 3D shape. The key idea of our method is a pretraining strategy that allows our transformer to learn plausible candidate articulations for 3D shapes based on a geometric-driven search without manual articulation annotation. The search automatically discovers physically valid part motions that do not cause detachments or collisions with other shape parts. Our experiments indicate that this geometric pretraining strategy, along with carefully designed choices in our transformer architecture, yields state-of-the-art results in articulation inference in the PartNet-Mobility dataset.