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PD2^{2}GS: Part-Level Decoupling and Continuous Deformation of Articulated Objects via Gaussian Splatting

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
articulated object; reconstruction; digital twin;
Abstract:

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 PD2^{2}GS, 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, PD2^{2}GS 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 PD2^{2}GS surpasses prior methods in geometric and kinematic accuracy, and in consistency under continuous control, both on synthetic and real data.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces PD²GS, a framework that learns a shared canonical Gaussian field and models arbitrary interaction states as continuous deformations, jointly encoding geometry and kinematics. It resides in the 'Part-Level Decoupling with Continuous Deformation' leaf under Gaussian Splatting-Based Articulated Reconstruction, which contains only two papers total. This is a relatively sparse research direction within the broader taxonomy of 37 papers, suggesting the specific combination of Gaussian splatting with continuous deformation for articulated objects is an emerging area rather than a crowded subfield.

The taxonomy reveals several neighboring approaches: sibling leaves include 'Self-Supervised Multi-Part Segmentation' (progressive primitive segmentation) and 'Interactive Visual-Physical Modeling' (manipulation-focused frameworks). Parallel branches offer alternative representations—Neural Implicit and Radiance Field Methods encode deformations in MLPs rather than explicit Gaussian primitives, while Part-Level Reconstruction and Motion Analysis emphasizes joint shape-motion estimation without necessarily using Gaussian representations. The paper's Gaussian-based approach contrasts with volumetric neural fields by trading smoothness guarantees for faster rendering and more explicit part control, positioning it at the intersection of explicit primitives and continuous deformation modeling.

Among 20 candidates examined, the core PD²GS framework contribution shows 2 refutable candidates out of 10 examined, indicating some prior work in Gaussian-based articulated reconstruction exists within this limited search scope. The coarse-to-fine segmentation procedure was not examined against any candidates (0 examined), leaving its novelty unassessed in this analysis. The RS-Art dataset contribution examined 10 candidates with 1 appearing to provide overlapping evaluation resources. These statistics reflect a focused semantic search rather than exhaustive coverage, suggesting the framework builds on an emerging but not entirely unexplored foundation in Gaussian splatting for articulated objects.

Based on the limited 20-candidate search, the work appears to advance a relatively sparse research direction by combining continuous deformation with part-level Gaussian decoupling. The analysis does not cover the full breadth of articulated object reconstruction literature, particularly classical mesh-based or marker-based systems that may address similar problems through different paradigms. The taxonomy structure suggests the paper occupies a niche intersection of explicit primitives and self-supervised articulation modeling, though the extent of its novelty relative to the broader field remains partially characterized by this top-K semantic search.

Taxonomy

Core-task Taxonomy Papers
37
3
Claimed Contributions
20
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: continuous deformation modeling of articulated objects from multi-view images. The field encompasses a diverse set of approaches organized around representation choices and reconstruction strategies. Gaussian Splatting-Based Articulated Reconstruction has emerged as a prominent branch, leveraging explicit point-based primitives for efficient rendering and part-level decoupling. Neural Implicit and Radiance Field Methods offer volumetric alternatives that encode geometry and appearance in continuous functions, while Part-Level Reconstruction and Motion Analysis focuses on segmenting objects into kinematic components. Mesh-Based Articulated Animation[2] and Multi-Camera Tracking and Pose Estimation represent more classical pipelines, often relying on template models or marker-based systems. Non-Rigid and Deformable Object Reconstruction addresses broader deformation beyond articulation, and specialized branches cover Active Vision and Dynamic Reconfiguration[31], Multi-View 3D Representation Learning, Generative and Text-Guided Synthesis, and domain-specific Capture Systems. Recent work reveals contrasting philosophies in handling articulation: some methods impose strong skeletal priors or part decompositions, while others learn deformations in a more holistic, template-free manner. Within the Gaussian splatting branch, PD2GS[0] emphasizes part-level decoupling with continuous deformation, aiming to disentangle rigid transformations from local shape changes. This positions it closely alongside REArtGS[1], which similarly targets articulated reconstruction using Gaussian primitives but may differ in how part boundaries and motion are regularized. Compared to neural implicit approaches like Banmo[3] or PARIS[4], which encode deformation fields in MLPs, Gaussian-based methods trade off smoothness guarantees for faster rendering and more explicit control over part assignments. The interplay between representation fidelity, computational efficiency, and the ability to generalize across object categories remains an open question, with PD2GS[0] contributing to the ongoing exploration of how explicit primitives can capture complex, continuous articulated motion from sparse multi-view observations.

Claimed Contributions

PD²GS framework for part-level decoupling and continuous deformation

The authors propose a self-supervised framework that represents articulated objects using a canonical 3D Gaussian field, with each interaction state modeled as a continuous deformation of this field via latent-conditioned transformations. This unified representation enables part-aware reconstruction, continuous control, and kinematic modeling without manual supervision.

10 retrieved papers
Can Refute
Coarse-to-fine segmentation procedure for part-level Gaussian decoupling

The method introduces a two-stage segmentation approach that first clusters Gaussian primitives by trajectory similarity using VLM guidance, then refines part boundaries through visibility-aware SAM prompting and boundary-aware Gaussian splitting. This produces sharp part interfaces while maintaining smooth motion and mutual exclusivity between parts.

0 retrieved papers
RS-Art real-to-sim evaluation dataset

The authors release RS-Art, a new benchmark dataset that pairs multi-view RGB-D captures of real articulated objects with their reverse-engineered 3D models in URDF format. This dataset enables rigorous evaluation of sim-to-real performance and supports physically grounded assessment in simulators.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

PD²GS framework for part-level decoupling and continuous deformation

The authors propose a self-supervised framework that represents articulated objects using a canonical 3D Gaussian field, with each interaction state modeled as a continuous deformation of this field via latent-conditioned transformations. This unified representation enables part-aware reconstruction, continuous control, and kinematic modeling without manual supervision.

Contribution

Coarse-to-fine segmentation procedure for part-level Gaussian decoupling

The method introduces a two-stage segmentation approach that first clusters Gaussian primitives by trajectory similarity using VLM guidance, then refines part boundaries through visibility-aware SAM prompting and boundary-aware Gaussian splitting. This produces sharp part interfaces while maintaining smooth motion and mutual exclusivity between parts.

Contribution

RS-Art real-to-sim evaluation dataset

The authors release RS-Art, a new benchmark dataset that pairs multi-view RGB-D captures of real articulated objects with their reverse-engineered 3D models in URDF format. This dataset enables rigorous evaluation of sim-to-real performance and supports physically grounded assessment in simulators.

Table of Contents

PD$^{2}$GS: Part-Level Decoupling and Continuous Deformation of Articulated Objects via Gaussian Splatting | Novelty Validation