A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
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Abstract (arXiv)
Recent work has made significant progress on using implicit functions, as a continuous representation for 3D rigid object shape reconstruction. However, much less effort has been devoted to modeling general articulated objects. Compared to rigid objects, articulated objects have higher degrees of freedom, which makes it hard to generalize to unseen shapes. To deal with the large shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to represent articulated shapes with a disentangled latent space, where we have separate codes for encoding shape and articulation. We assume no prior knowledge on part geometry, articulation status, joint type, joint axis, and joint location. With this disentangled continuous representation, we demonstrate that we can control the articulation input and animate unseen instances with unseen joint angles. Furthermore, we propose a Test-Time Adaptation inference algorithm to adjust our model during inference. We demonstrate our model generalize well to out-of-distribution and unseen data, e.g., partial point clouds and real-world depth images.
Key facts. Cited by 113 papers in the map (data through 7 Oct 2026). Builds on 72 of its 96 references that are in the map. First posted on arXiv 15 Apr 2021.
Builds on
72 of 96 references.
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50 most cited of 72 All 72 (JSON)
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Citing papers per year: 2021: 11; 2022: 22; 2023: 19; 2024: 18; 2025: 27; 2026: 16.
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- 2024ShapeFusion: A 3D diffusion model for localized shape editingcited by 6
- 2025HierOctFusion: Multi-scale Octree-based 3D Shape Generation via Part-Whole-Hierarchy Message Passingcited by 2
- 2025CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Modelscited by 1
- 2025ArticFlow: Generative Simulation of Articulated Mechanismscited by 1
- 2025GAOT: Generating Articulated Objects Through Text-Guided Diffusion Modelscited by 1
- 2025GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models3 Dec 2025
- 2025ArticFlow: Generative Simulation of Articulated Mechanisms22 Nov 2025
- 2025FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion29 Oct 2025
- 2025CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Models8 Sep 2025
- 2025HierOctFusion: Multi-scale Octree-based 3D Shape Generation via Part-Whole-Hierarchy Message Passing14 Aug 2025
- 2025Guiding Diffusion-Based Articulated Object Generation by Partial Point Cloud Alignment and Physical Plausibility Constraints1 Aug 2025
- 2024ShapeFusion: A 3D diffusion model for localized shape editing28 Mar 2024
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- 2023PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objectscited by 115
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- 2023Diffusion-Guided Reconstruction of Everyday Hand-Object Interaction Clipscited by 73
- 2025ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splattingcited by 67
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- 2026StructureGS: Structure-aware Gaussian Splatting for Articulated Object Reconstruction29 Jul 2026
- 2026UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image29 Jun 2026
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- 2026SCAPO: Self-Supervised Category-Level Articulated Pose Estimation from a Single 3D Observation1 Jun 2026
- 2026Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video18 May 2026
- 2026ArtMesh: Part-Aware Articulated Mesh Fields with Motion-Consistent Dynamics15 May 2026
- 2026GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting9 Apr 2026
- 2026Articulation in Motion: Prior-free Part Mobility Analysis for Articulated Objects By Dynamic-Static Disentanglement3 Mar 2026
- 2026ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals26 Feb 2026
- 2026PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling12 Feb 2026
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