Self-Supervised Multi-Part Articulated Objects Modeling via Deformable Gaussian Splatting and Progressive Primitive Segmentation

• URL: http://arxiv.org/abs/2506.09663v1
• Date: Wed, 11 Jun 2025 12:32:16 GMT
• Title: Self-Supervised Multi-Part Articulated Objects Modeling via Deformable Gaussian Splatting and Progressive Primitive Segmentation
• Authors: Haowen Wang, Xiaoping Yuan, Zhao Jin, Zhen Zhao, Zhengping Che, Yousong Xue, Jin Tian, Yakun Huang, Jian Tang,
• Abstract summary: We introduce DeGSS, a unified framework that encodes articulated objects as deformable 3D Gaussian fields, embedding geometry, appearance, and motion in one compact representation.<n>To evaluate generalization and realism, we enlarge the synthetic PartNet-Mobility benchmark and release RS-Art, a real-to-sim dataset that pairs RGB captures with accurately reverse-engineered 3D models.
• Score: 23.18517560629462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Articulated objects are ubiquitous in everyday life, and accurate 3D representations of their geometry and motion are critical for numerous applications. However, in the absence of human annotation, existing approaches still struggle to build a unified representation for objects that contain multiple movable parts. We introduce DeGSS, a unified framework that encodes articulated objects as deformable 3D Gaussian fields, embedding geometry, appearance, and motion in one compact representation. Each interaction state is modeled as a smooth deformation of a shared field, and the resulting deformation trajectories guide a progressive coarse-to-fine part segmentation that identifies distinct rigid components, all in an unsupervised manner. The refined field provides a spatially continuous, fully decoupled description of every part, supporting part-level reconstruction and precise modeling of their kinematic relationships. To evaluate generalization and realism, we enlarge the synthetic PartNet-Mobility benchmark and release RS-Art, a real-to-sim dataset that pairs RGB captures with accurately reverse-engineered 3D models. Extensive experiments demonstrate that our method outperforms existing methods in both accuracy and stability.

Related papers

• Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting [9.432567695900184]
  Part$2$GS is a novel framework for modeling articulated digital twins of multi-part objects with high-fidelity geometry.<n>To ensure physically consistent motion, we propose a motion-aware canonical representation guided by physics-based constraints.<n>We show that Part$2$GS consistently outperforms state-of-the-art methods by up to 10$times$ in Chamfer Distance for movable parts.
  arXiv  Detail & Related papers  (2025-06-20T17:59:12Z)
• PIG: Physically-based Multi-Material Interaction with 3D Gaussians [14.097146027458368]
  PIG: Physically-Based Multi-Material Interaction with 3D Gaussians is a novel approach that combines 3D object segmentation with the simulation of objects interacting in high precision.<n>We show that our method not only outperforms the state-of-the-art (SOTA) in terms of visual quality, but also opens up new directions and pipelines for the field of physically realistic scene generation.
  arXiv  Detail & Related papers  (2025-06-09T11:25:21Z)
• IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments [56.85804719947]
  We present IAAO, a framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction.<n>We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images.<n>We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances.
  arXiv  Detail & Related papers  (2025-04-09T12:36:48Z)
• PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation [79.46526296655776]
  PRISM is a novel approach for 3D shape generation that integrates categorical diffusion models with Statistical Shape Models (SSM) and Gaussian Mixture Models (GMM)<n>Our method employs compositional SSMs to capture part-level geometric variations and uses GMM to represent part semantics in a continuous space.<n>Our approach significantly outperforms previous methods in both quality and controllability of part-level operations.
  arXiv  Detail & Related papers  (2025-04-06T11:48:08Z)
• ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting [29.69981069695724]
  We tackle the challenge of concurrent reconstruction at the part level with the RGB appearance and estimation of motion parameters.<n>We reconstruct the articulated object in 3D Gaussian representations with both appearance and geometry information at the same time.<n>We introduce ArticulatedGS, a self-supervised, comprehensive framework that autonomously learns to model shapes and appearances at the part level.
  arXiv  Detail & Related papers  (2025-03-11T07:56:12Z)
• ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
  Building articulated objects is a key challenge in computer vision.<n>Existing methods often fail to effectively integrate information across different object states.<n>We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation.
  arXiv  Detail & Related papers  (2025-02-26T10:25:32Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• REACTO: Reconstructing Articulated Objects from a Single Video [64.89760223391573]
  We propose a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation of the joints. Our method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects.
  arXiv  Detail & Related papers  (2024-04-17T08:01:55Z)
• SM$^3$: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects [24.737865259695006]
  We propose a self-supervised interaction perception method, referred to as SM$3$, to model articulated objects. By constructing 3D geometries and textures from the captured 2D images, SM$3$ achieves integrated optimization of movable part and joint parameters. Evaluations demonstrate that SM$3$ surpasses existing benchmarks across various categories and objects, while its adaptability in real-world scenarios has been thoroughly validated.
  arXiv  Detail & Related papers  (2024-01-17T11:15:09Z)
• Generative Category-Level Shape and Pose Estimation with Semantic Primitives [27.692997522812615]
  We propose a novel framework for category-level object shape and pose estimation from a single RGB-D image. To handle the intra-category variation, we adopt a semantic primitive representation that encodes diverse shapes into a unified latent space. We show that the proposed method achieves SOTA pose estimation performance and better generalization in the real-world dataset.
  arXiv  Detail & Related papers  (2022-10-03T17:51:54Z)
• SPAMs: Structured Implicit Parametric Models [30.19414242608965]
  We learn Structured-implicit PArametric Models (SPAMs) as a deformable object representation that structurally decomposes non-rigid object motion into part-based disentangled representations of shape and pose. Experiments demonstrate that our part-aware shape and pose understanding lead to state-of-the-art performance in reconstruction and tracking of depth sequences of complex deforming object motion.
  arXiv  Detail & Related papers  (2022-01-20T12:33:46Z)
• Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering [53.16864661460889]
  Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. In this paper, we introduce body segmentation as critical supervision. To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
  arXiv  Detail & Related papers  (2020-03-24T14:25:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.