mlsanityData to 14 Oct 2026

TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations

arXiv 2406.12121 ↗First posted 17 Jun 2024cs.CVcited by 3

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Abstract (arXiv)

This work proposes a novel representation of injective deformations of 3D space, which overcomes existing limitations of injective methods: inaccuracy, lack of robustness, and incompatibility with general learning and optimization frameworks. The core idea is to reduce the problem to a deep composition of multiple 2D mesh-based piecewise-linear maps. Namely, we build differentiable layers that produce mesh deformations through Tutte's embedding (guaranteed to be injective in 2D), and compose these layers over different planes to create complex 3D injective deformations of the 3D volume. We show our method provides the ability to efficiently and accurately optimize and learn complex deformations, outperforming other injective approaches. As a main application, we produce complex and artifact-free NeRF and SDF deformations.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 62 of its 95 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

62 of 95 references.

References · 62

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2018Neural Ordinary Differential Equationscited by 3,779
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2015Variational Inference with Normalizing Flowscited by 2,170
  6. 2016Density estimation using Real NVPcited by 2,042
  7. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  8. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  9. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
  10. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
  11. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,157
  12. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
38 more
  1. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  2. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  3. 2017Masked Autoregressive Flow for Density Estimationcited by 684
  4. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 640
  5. 2020Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humanscited by 634
  6. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 600
  7. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 594
  8. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  9. 2022HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Videocited by 483
  10. 2020Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Videocited by 471
  11. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  12. 2018Invertible Residual Networkscited by 365
  13. 2018Learning Category-Specific Mesh Reconstruction from Image Collectionscited by 317
  14. 2015MADE: Masked Autoencoder for Distribution Estimationcited by 307
  15. 2022NeuMan: Neural Human Radiance Field from a Single Videocited by 244
  16. 2020Learned Initializations for Optimizing Coordinate-Based Neural Representationscited by 242
  17. 2021A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Posecited by 233
  18. 2021Editing Conditional Radiance Fieldscited by 222
  19. 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 217
  20. 2023Tracking Everything Everywhere All at Oncecited by 193
  21. 20183D-CODED : 3D Correspondences by Deep Deformationcited by 160
  22. 2021Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Renderingcited by 158
  23. 2021H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motioncited by 157
  24. 2022SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Videocited by 144
  25. 2022NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editingcited by 142
  26. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
  27. 2021HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputscited by 112
  28. 2021Animatable Neural Radiance Fields from Monocular RGB Videoscited by 109
  29. 2019Neural Cages for Detail-Preserving 3D Deformationscited by 106
  30. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
  31. 2023SHERF: Generalizable Human NeRF from a Single Imagecited by 88
  32. 2022Deforming Radiance Fields with Cagescited by 78
  33. 2020Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flowscited by 61
  34. 2022A Survey of Non-Rigid 3D Registrationcited by 59
  35. 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 57
  36. 2018Conditional Density Estimation with Bayesian Normalising Flowscited by 54
  37. 2021Learning Skeletal Articulations with Neural Blend Shapescited by 53
  38. 2021KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Controlcited by 51

50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025PoissonNet: A Local-Global Approach for Learning on Surfacescited by 6
  2. 2025Handle-based Mesh Deformation Guided By Vision Language Modelcited by 0
  3. 2026GenSP: Consistent Spherical Parameterization via Learning Shape Generative Modelscited by 0