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Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence

arXiv 2011.13650 ↗First posted 27 Nov 2020cs.CVcited by 119

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Abstract (arXiv)

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 35; 2023: 41; 2024: 12; 2025: 12; 2026: 12.

119 papers.

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  1. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  2. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 459
  3. 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 216
  4. 2021Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulationcited by 204
  5. 2025Native and Compact Structured Latents for 3D Generationcited by 157
  6. 2022NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editingcited by 141
  7. 2023SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Fieldcited by 93
  8. 2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
  9. 2022ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representationscited by 64
  10. 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 56
  11. 2022Self-supervised Neural Articulated Shape and Appearance Modelscited by 51
  12. 2022SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fieldscited by 51
38 more
  1. 2022Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Imagescited by 39
  2. 2022AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesiscited by 39
  3. 2022Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flowcited by 38
  4. 2023Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generationcited by 37
  5. 2023PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervisioncited by 36
  6. 2022Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshescited by 34
  7. 2023Mesh2Tex: Generating Mesh Textures from Image Queriescited by 31
  8. 2022EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Pointscited by 30
  9. 2022Topologically-Aware Deformation Fields for Single-View 3D Reconstructioncited by 29
  10. 2022Neural Shape Deformation Priorscited by 26
  11. 2022Neural Capture of Animatable 3D Human from Monocular Videocited by 22
  12. 2021SP-GAN: Sphere-Guided 3D Shape Generation and Manipulationcited by 21
  13. 2022Learning Whole Heart Mesh Generation From Patient Images For Computational Simulationscited by 21
  14. 2022Neural Correspondence Field for Object Pose Estimationcited by 21
  15. 2023SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defectscited by 17
  16. 2022Neural Parameterization for Dynamic Human Head Editingcited by 16
  17. 2023Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Viewscited by 16
  18. 2023AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collectionscited by 16
  19. 2024Geometry Transfer for Stylizing Radiance Fieldscited by 16
  20. 2023RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereocited by 15
  21. 20234D Myocardium Reconstruction with Decoupled Motion and Shape Modelcited by 15
  22. 2023Few-Shot In-Context Imitation Learning via Implicit Graph Alignmentcited by 14
  23. 2025Hierarchical Neural Semantic Representation for 3D Semantic Correspondencecited by 14
  24. 2022Implicit field supervision for robust non-rigid shape matchingcited by 13
  25. 20223D Concept Grounding on Neural Fieldscited by 13
  26. 2023T2TD: Text-3D Generation Model based on Prior Knowledge Guidancecited by 13
  27. 2024Localized Gaussian Splatting Editing with Contextual Awarenesscited by 13
  28. 2022An Implicit Parametric Morphable Dental Modelcited by 12
  29. 2024SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimationcited by 12
  30. 2023GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Modelscited by 11
  31. 2023DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Imagecited by 11
  32. 2022Deep Deformable 3D Caricatures with Learned Shape Controlcited by 10
  33. 2023Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflectioncited by 10
  34. 2023DPF-Net: Combining Explicit Shape Priors in Deformable Primitive Field for Unsupervised Structural Reconstruction of 3D Objectscited by 10
  35. 2023ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representationscited by 10
  36. 2023Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Priorcited by 9
  37. 2024Generic Objects as Pose Probes for Few-shot View Synthesiscited by 9
  38. 2025Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Spacecited by 9

50 most cited of 119 All 119 (JSON)