# PD2GS: Part-Level Decoupling and Continuous Deformation of Articulated Objects via Gaussian Splatting

arXiv: https://arxiv.org/abs/2506.09663 · HTML: https://mlsanity.com/paper/2506.09663/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [DreamArt: Generating Interactable Articulated Objects from a Single Image](https://mlsanity.com/paper/2507.05763/) (2025)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [GAUGE: Group-Wise View-Inconsistency Rectification for Feed-Forward 4D Tracking](https://mlsanity.com/paper/2609.32596/) (2026)

## Abstract (arXiv)

Articulated objects are ubiquitous and important in robotics, AR/VR, and digital twins. Most self-supervised methods for articulated object modeling reconstruct discrete interaction states and relate them via cross-state geometric consistency, yielding representational fragmentation and drift that hinder smooth control of articulated configurations. We introduce PD2GS, a novel framework that learns a shared canonical Gaussian field and models the arbitrary interaction state as its continuous deformation, jointly encoding geometry and kinematics. By associating each interaction state with a latent code and refining part boundaries using generic vision priors, PD2GS enables accurate and reliable part-level decoupling while enforcing mutual exclusivity between parts and preserving scene-level coherence. This unified formulation supports part-aware reconstruction, fine-grained continuous control, and accurate kinematic modeling, all without manual supervision. To assess realism and generalization, we release RS-Art, a real-to-sim RGB-D dataset aligned with reverse-engineered 3D models, supporting real-world evaluation. Extensive experiments demonstrate that PD2GS surpasses prior methods in geometric and kinematic accuracy, and in consistency under continuous control, both on synthetic and real data.