mlsanityData to 7 Oct 2026

Variational Autoencoders for Deforming 3D Mesh Models

arXiv 1709.04307 ↗First posted 13 Sep 2017cs.GRcited by 69

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Abstract (arXiv)

3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-of-the-art methods.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2017.

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Citing papers per year: 2021: 16; 2022: 12; 2023: 15; 2024: 13; 2025: 10; 2026: 3.

69 papers.

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  1. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  2. 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 216
  3. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 198
  4. 2023TextDeformer: Geometry Manipulation using Text Guidancecited by 94
  5. 2024SPAD : Spatially Aware Multiview Diffuserscited by 62
  6. 2022Uncertainty Inspired Underwater Image Enhancementcited by 53
  7. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  8. 2024VideoMV: Consistent Multi-View Generation Based on Large Video Generative Modelcited by 45
  9. 2021ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generatorscited by 40
  10. 2021DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinatescited by 36
  11. 2024Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Imagescited by 34
  12. 20213D Pose Transfer with Correspondence Learning and Mesh Refinementcited by 30
38 more
  1. 2022Skeleton-free Pose Transfer for Stylized 3D Characterscited by 29
  2. 2022SPAGHETTI: Editing Implicit Shapes Through Part Aware Generationcited by 28
  3. 2022Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshescited by 28
  4. 2024Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splattingcited by 26
  5. 2023Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wildcited by 25
  6. 2021Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderingscited by 23
  7. 2022Self-Supervised Representation Learning for CADcited by 22
  8. 2022Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstructioncited by 21
  9. 2025Pippo: High-Resolution Multi-View Humans from a Single Imagecited by 20
  10. 2021GLASS: Geometric Latent Augmentation for Shape Spacescited by 16
  11. 2023TapMo: Shape-aware Motion Generation of Skeleton-free Characterscited by 15
  12. 2021Learning Feature Aggregation for Deep 3D Morphable Modelscited by 13
  13. 2024NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generationcited by 13
  14. 2025Edit360: 2D Image Edits to 3D Assets from Any Anglecited by 13
  15. 2024PoNQ: a Neural QEM-based Mesh Representationcited by 12
  16. 2022Deep Structural Causal Shape Modelscited by 11
  17. 2023GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Modelscited by 11
  18. 2023Pushing the Limits of 3D Shape Generation at Scalecited by 11
  19. 2021A Deep Signed Directional Distance Function for Object Shape Representationcited by 10
  20. 2021Geometry-Contrastive Transformer for Generalized 3D Pose Transfercited by 10
  21. 2021Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizescited by 8
  22. 20223D Shape Sequence of Human Comparison and Classification using Current and Varifoldscited by 8
  23. 2022Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearancecited by 8
  24. 2023MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generationcited by 8
  25. 2021Intuitive Shape Editing in Latent Spacecited by 7
  26. 2023Interactive Geometry Editing of Neural Radiance Fieldscited by 7
  27. 2024LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Imagecited by 7
  28. 2021Neural Human Deformation Transfercited by 6
  29. 2023Variational Barycentric Coordinatescited by 6
  30. 2023ET3D: Efficient Text-to-3D Generation via Multi-View Distillationcited by 6
  31. 2024Multi-scale Latent Point Consistency Models for 3D Shape Generationcited by 6
  32. 2025PoissonNet: A Local-Global Approach for Learning on Surfacescited by 6
  33. 2021Deep Deformation Detail Synthesis for Thin Shell Modelscited by 5
  34. 2021Learning Mesh Representations via Binary Space Partitioning Tree Networkscited by 5
  35. 2021Active Learning of Neural Collision Handler for Complex 3D Mesh Deformationscited by 5
  36. 20233D-QAE: Fully Quantum Auto-Encoding of 3D Point Cloudscited by 5
  37. 2025Neural Face Skinning for Mesh-agnostic Facial Expression Cloningcited by 5
  38. 2022Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelinescited by 4

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