mlsanityData to 7 Oct 2026

Neural Shape Deformation Priors

arXiv 2210.05616 ↗First posted 11 Oct 2022cs.CVcited by 26

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Abstract (arXiv)

We present Neural Shape Deformation Priors, a novel method for shape manipulation that predicts mesh deformations of non-rigid objects from user-provided handle movements. State-of-the-art methods cast this problem as an optimization task, where the input source mesh is iteratively deformed to minimize an objective function according to hand-crafted regularizers such as ARAP. In this work, we learn the deformation behavior based on the underlying geometric properties of a shape, while leveraging a large-scale dataset containing a diverse set of non-rigid deformations. Specifically, given a source mesh and desired target locations of handles that describe the partial surface deformation, we predict a continuous deformation field that is defined in 3D space to describe the space deformation. To this end, we introduce transformer-based deformation networks that represent a shape deformation as a composition of local surface deformations. It learns a set of local latent codes anchored in 3D space, from which we can learn a set of continuous deformation functions for local surfaces. Our method can be applied to challenging deformations and generalizes well to unseen deformations. We validate our approach in experiments using the DeformingThing4D dataset, and compare to both classic optimization-based and recent neural network-based methods.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 53 of its 82 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

53 of 82 references.

References · 53

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38 more
  1. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  2. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
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  4. 2020Implicit Geometric Regularization for Learning Shapescited by 707
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  9. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
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  15. 20214DComplete: Non-Rigid Motion Estimation Beyond the Observable Surfacecited by 123
  16. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
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  20. 2021NPMs: Neural Parametric Models for 3D Deformable Shapescited by 89
  21. 2020Deep Implicit Templates for 3D Shape Representationcited by 86
  22. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 12; 2024: 9; 2025: 1; 2026: 3.

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  1. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  2. 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 159
  3. 2023Rotation-Invariant Transformer for Point Cloud Matchingcited by 68
  4. 2022RGBD2: Generative Scene Synthesis via Incremental View Inpainting using RGBD Diffusion Modelscited by 45
  5. 2024GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusioncited by 36
  6. 2024Recent Trends in 3D Reconstruction of General Non-Rigid Scenescited by 28
  7. 2024Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamicscited by 28
  8. 2024Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Trackingcited by 27
  9. 2023As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priorscited by 24
  10. 2023InstructP2P: Learning to Edit 3D Point Clouds with Text Instructionscited by 14
  11. 2023Functional Diffusioncited by 13
  12. 2023CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular Videoscited by 10
14 more
  1. 2023DPHMs: Diffusion Parametric Head Models for Depth-based Trackingcited by 10
  2. 2024Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysiscited by 10
  3. 2024DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generationcited by 9
  4. 2024DNF: Unconditional 4D Generation with Dictionary-based Neural Fieldscited by 8
  5. 2023Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attentioncited by 7
  6. 2024Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Posescited by 6
  7. 2025MeshPad: Interactive Sketch-Conditioned Artist-Reminiscent Mesh Generation and Editingcited by 6
  8. 2023PHRIT: Parametric Hand Representation with Implicit Templatecited by 5
  9. 2024Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshingcited by 4
  10. 2023Nonrigid Object Contact Estimation With Regional Unwrapping Transformercited by 3
  11. 2023CeRF: Convolutional Neural Radiance Fields for New View Synthesis with Derivatives of Ray Modelingcited by 0
  12. 2026Deformation-based In-Context Learning for Point Cloud Understandingcited by 0
  13. 2026CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representationcited by 0
  14. 2026As-Rigid-As-Possible Regularization for Implicit Surfacescited by 0