mlsanityData to 7 Oct 2026

DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion

arXiv 2305.01921 ↗First posted 3 May 2023cs.CVcited by 32

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Abstract (arXiv)

While the community of 3D point cloud generation has witnessed a big growth in recent years, there still lacks an effective way to enable intuitive user control in the generation process, hence limiting the general utility of such methods. Since an intuitive way of decomposing a shape is through its parts, we propose to tackle the task of controllable part-based point cloud generation. We introduce DiffFacto, a novel probabilistic generative model that learns the distribution of shapes with part-level control. We propose a factorization that models independent part style and part configuration distributions and presents a novel cross-diffusion network that enables us to generate coherent and plausible shapes under our proposed factorization. Experiments show that our method is able to generate novel shapes with multiple axes of control. It achieves state-of-the-art part-level generation quality and generates plausible and coherent shapes while enabling various downstream editing applications such as shape interpolation, mixing, and transformation editing. Project website: https://difffacto.github.io/

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 40 of its 61 references that are in the map. First posted on arXiv 3 May 2023.

Builds on

40 of 61 references.

References · 40

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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  6. 2022Generative Adversarial Networkscited by 5,603
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28 more
  1. 2014Conditional Generative Adversarial Netscited by 2,152
  2. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  3. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  4. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
  5. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  6. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 584
  7. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  8. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
  9. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  10. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  11. 2020Learning Gradient Fields for Shape Generationcited by 245
  12. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  13. 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
  14. 2018PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attentioncited by 105
  15. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  16. 2020SoftFlow: Probabilistic Framework for Normalizing Flow on Manifoldscited by 97
  17. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  18. 2019ShapeGlot: Learning Language for Shape Differentiationcited by 54
  19. 2018Implicit Maximum Likelihood Estimationcited by 52
  20. 2019Learning Part Generation and Assembly for Structure-aware Shape Synthesiscited by 35
  21. 2020DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generationcited by 31
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  23. 2021PartGlot: Learning Shape Part Segmentation from Language Reference Gamescited by 30
  24. 2016Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layoutscited by 21
  25. 2021EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generationcited by 21
  26. 2022NeuForm: Adaptive Overfitting for Neural Shape Editingcited by 18
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 12; 2026: 6.

32 papers.

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  1. 2023XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchiescited by 183
  2. 2025PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformerscited by 78
  3. 2024PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Modelscited by 60
  4. 2024Part123: Part-aware 3D Reconstruction from a Single-view Imagecited by 54
  5. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
  6. 2025Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modelingcited by 41
  7. 2025AutoPartGen: Autogressive 3D Part Generation and Discoverycited by 33
  8. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectivescited by 27
  9. 2025From One to More: Contextual Part Latents for 3D Generationcited by 26
  10. 2024SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generationcited by 24
  11. 2025FullPart: Generating each 3D Part at Full Resolutioncited by 17
  12. 2024Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidancecited by 16
20 more
  1. 2025MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Cloudscited by 15
  2. 2023InstructP2P: Learning to Edit 3D Point Clouds with Text Instructionscited by 14
  3. 2024Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodingscited by 10
  4. 2023FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generationcited by 9
  5. 2025PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimizationcited by 8
  6. 2024Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fieldscited by 5
  7. 2025SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generationcited by 4
  8. 20243D Object Detection from Point Cloud via Voting Step Diffusioncited by 3
  9. 2024Non-rigid Relative Placement through 3D Dense Diffusioncited by 2
  10. 2024JADE: Joint-aware Latent Diffusion for 3D Human Generative Modelingcited by 2
  11. 2025H2OFlow: Grounding Human-Object Affordances with 3D Generative Models and Dense Diffused Flowscited by 1
  12. 2026Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidancecited by 1
  13. 2026BoxSplitGen: A Generative Model for 3D Part Bounding Boxes in Varying Granularitycited by 1
  14. 2026MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twinscited by 1
  15. 2025PRISM: Probabilistic Representation for Integrated Shape Modeling and Generationcited by 0
  16. 2025StrucADT: Generating Structure-controlled 3D Point Clouds with Adjacency Diffusion Transformercited by 0
  17. 2025Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Datasetcited by 0
  18. 2026Efficient Coarse-to-Fine Diffusion Models with Time Step Sequence Redistributioncited by 0
  19. 2026CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Controlcited by 0
  20. 2026Point Diffusion Mamba: Unified Diffusion-State-Space Modeling for Single-View 3D Reconstruction under Data Scarcitycited by 0