mlsanityData to 7 Oct 2026

On Diffusion Process in SE(3)-invariant Space

arXiv 2403.01430 ↗First posted 3 Mar 2024cs.LGcited by 1

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Abstract (arXiv)

Sampling viable 3D structures (e.g., molecules and point clouds) with SE(3)-invariance using diffusion-based models proved promising in a variety of real-world applications, wherein SE(3)-invariant properties can be naturally characterized by the inter-point distance manifold. However, due to the non-trivial geometry, we still lack a comprehensive understanding of the diffusion mechanism within such SE(3)-invariant space. This study addresses this gap by mathematically delineating the diffusion mechanism under SE(3)-invariance, via zooming into the interaction behavior between coordinates and the inter-point distance manifold through the lens of differential geometry. Upon this analysis, we propose accurate and projection-free diffusion SDE and ODE accordingly. Such formulations enable enhancing the performance and the speed of generation pathways; meanwhile offering valuable insights into other systems incorporating SE(3)-invariance.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 3 Mar 2024.

Builds on

32 of 50 references.

References · 32

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  7. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  8. 2023Consistency Modelscited by 1,778
  9. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  10. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  11. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  12. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
20 more
  1. 2020Directional Message Passing for Molecular Graphscited by 556
  2. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  3. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  4. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  5. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  6. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  7. 2022Riemannian Score-Based Generative Modellingcited by 216
  8. 2023SE(3) diffusion model with application to protein backbone generationcited by 214
  9. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  10. 2019Learnable Triangulation of Human Posecited by 160
  11. 2023DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generationcited by 110
  12. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  13. 2019Self-Supervised Learning of 3D Human Pose using Multi-view Geometrycited by 98
  14. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Modelscited by 97
  15. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  16. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  17. 2022Direct Molecular Conformation Generationcited by 30
  18. 2019Generating valid Euclidean distance matricescited by 19
  19. 2023Learning to Decouple Complex Systemscited by 3
  20. 2023Molecular Conformation Generation via Shifting Scorescited by 2

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