mlsanityData to 7 Oct 2026

DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

arXiv 2102.09105 ↗First posted 18 Feb 2021cs.CV cs.GRcited by 36

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Abstract (arXiv)

We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points' translations to the entire mesh. To avoid learning zero deformation as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations' plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 18 Feb 2021.

Builds on

28 of 49 references.

References · 28

  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. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  4. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
  5. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  6. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
  7. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 267
  8. 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
  9. 20183D-CODED : 3D Correspondences by Deep Deformationcited by 160
  10. 2019PQ-NET: A Generative Part Seq2Seq Network for 3D Shapescited by 118
  11. 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
  12. 2019Neural Cages for Detail-Preserving 3D Deformationscited by 105
16 more
  1. 2017Variational Autoencoders for Deforming 3D Mesh Modelscited by 69
  2. 2020Unsupervised Shape and Pose Disentanglement for 3D Meshescited by 61
  3. 20193DN: 3D Deformation Networkcited by 60
  4. 2019StructureNet: Hierarchical Graph Networks for 3D Shape Generationcited by 59
  5. 2018Robust Watertight Manifold Surface Generation Method for ShapeNet Modelscited by 58
  6. 2019Composite Shape Modeling via Latent Space Factorizationcited by 31
  7. 2019Geometric Disentanglement for Generative Latent Shape Modelscited by 30
  8. 2017DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Imagecited by 25
  9. 2020Learning Generative Models of Shape Handlescited by 25
  10. 2017GRASS: Generative Recursive Autoencoders for Shape Structurescited by 24
  11. 2018Learning Free-Form Deformations for 3D Object Reconstructioncited by 20
  12. 2017Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networkscited by 17
  13. 2019Synthesizing 3D Shapes from Silhouette Image Collections using Multi-projection Generative Adversarial Networkscited by 14
  14. 2018ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learningcited by 11
  15. 2017Mesh-based Autoencoders for Localized Deformation Component Analysiscited by 7
  16. 2020DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spacescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 12; 2024: 7; 2025: 5; 2026: 5.

36 papers.

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  1. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
  2. 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusioncited by 283
  3. 2022Generative Neural Articulated Radiance Fieldscited by 101
  4. 2023TextDeformer: Geometry Manipulation using Text Guidancecited by 94
  5. 2022Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Searchcited by 61
  6. 2023Controllable Mesh Generation Through Sparse Latent Point Diffusion Modelscited by 40
  7. 2022Skeleton-free Pose Transfer for Stylized 3D Characterscited by 29
  8. 2022Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshescited by 28
  9. 2025Anymate: A Dataset and Baselines for Learning 3D Object Riggingcited by 27
  10. 2023As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priorscited by 24
  11. 2022Learning Whole Heart Mesh Generation From Patient Images For Computational Simulationscited by 21
  12. 2023InstructP2P: Learning to Edit 3D Point Clouds with Text Instructionscited by 14
24 more
  1. 2023Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapescited by 14
  2. 2024PASTA: Controllable Part-Aware Shape Generation with Autoregressive Transformerscited by 14
  3. 2023Weakly-supervised 3D Pose Transfer with Keypointscited by 12
  4. 2022LADIS: Language Disentanglement for 3D Shape Editingcited by 10
  5. 2023Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformationcited by 8
  6. 2024CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimizationcited by 8
  7. 2025Category-Agnostic Neural Object Riggingcited by 8
  8. 2021Intuitive Shape Editing in Latent Spacecited by 7
  9. 2023Explorable Mesh Deformation Subspaces from Unstructured Generative Modelscited by 6
  10. 2024Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Posescited by 6
  11. 2024ParSEL: Parameterized Shape Editing with Languagecited by 5
  12. 2024Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Imagescited by 5
  13. 2025ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Priorcited by 5
  14. 2024Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshingcited by 4
  15. 2024TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformationscited by 3
  16. 20233D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capabilitycited by 1
  17. 2025Relational Anatomical Supervision for Accurate 3D Multi-Chamber Cardiac Mesh Reconstructioncited by 1
  18. 2026Deep Feature Deformation Weightscited by 1
  19. 2023Learning-Based Biharmonic Augmentation for Point Cloud Classificationcited by 0
  20. 2025Handle-based Mesh Deformation Guided By Vision Language Modelcited by 0
  21. 2026Mat'ern Noise for Triangulation-Agnostic Flow Matching on Meshescited by 0
  22. 2026CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representationcited by 0
  23. 2026BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitivescited by 0
  24. 2026OptimusMesh: Compact Autoregressive Mesh Generation from Point Clouds via Sparse Latent Pivotscited by 0