mlsanityData to 7 Oct 2026

CA2T-Net: Category-Agnostic 3D Articulation Transfer from Single Image

arXiv 2301.02232 ↗First posted 5 Jan 2023cs.CV cs.AI cs.GRcited by 2

Not on the map. Why?

Abstract (arXiv)

We present a neural network approach to transfer the motion from a single image of an articulated object to a rest-state (i.e., unarticulated) 3D model. Our network learns to predict the object's pose, part segmentation, and corresponding motion parameters to reproduce the articulation shown in the input image. The network is composed of three distinct branches that take a shared joint image-shape embedding and is trained end-to-end. Unlike previous methods, our approach is independent of the topology of the object and can work with objects from arbitrary categories. Our method, trained with only synthetic data, can be used to automatically animate a mesh, infer motion from real images, and transfer articulation to functionally similar but geometrically distinct 3D models at test time.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 5 Jan 2023.

Builds on

25 of 33 references.

References · 25

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 584
  5. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
  6. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 490
  7. 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
  8. 2021Where2Act: From Pixels to Actions for Articulated 3D Objectscited by 179
  9. 2019Category-Level Articulated Object Pose Estimationcited by 158
  10. 2019Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapescited by 117
  11. 2021LASR: Learning Articulated Shape Reconstruction from a Monocular Videocited by 115
  12. 2016Resnet in Resnet: Generalizing Residual Architecturescited by 110
13 more
  1. 2021CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Cloudscited by 83
  2. 2020Articulation-aware Canonical Surface Mappingcited by 80
  3. 2020ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theorycited by 70
  4. 2018Deep Part Induction from Articulated Object Pairscited by 66
  5. 2021ROCA: Robust CAD Model Retrieval and Alignment from a Single Imagecited by 66
  6. 2020Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrievecited by 65
  7. 2022OPD: Single-view 3D Openable Part Detectioncited by 52
  8. 2022Self-supervised Neural Articulated Shape and Appearance Modelscited by 51
  9. 2019Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objectscited by 39
  10. 2021Towards Real-World Category-level Articulation Pose Estimationcited by 39
  11. 2021Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Imagecited by 39
  12. 2020RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloudcited by 26
  13. 2022Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collectionscited by 13

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023OPDMulti: Openable Part Detection for Multiple Objectscited by 38
  2. 2025MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videoscited by 6