mlsanityData to 7 Oct 2026

Interactive3D: Create What You Want by Interactive 3D Generation

arXiv 2404.16510 ↗First posted 25 Apr 2024cs.GR cs.CVcited by 21

Not on the map. Why?

Abstract (arXiv)

3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do not align with user expectations, thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability, or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process, restricting the scope for direct and versatile 3D modifications. In this work, we introduce Interactive3D, an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages, utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction, allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components, (ii) Deformable and Rigid Dragging, (iii) Geometric Transformations, and (iv) Semantic Editing. Subsequently, the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at urlhttps://interactive-3d.github.io/.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 35 of its 39 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

35 of 39 references.

References · 35

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2023Segment Anythingcited by 8,158
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  6. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  7. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  8. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  9. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  10. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  11. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  12. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
23 more
  1. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
  2. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
  3. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
  4. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  5. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
  6. 2023Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creationcited by 684
  7. 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
  8. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  9. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
  10. 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
  11. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  12. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  13. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 459
  14. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  15. 2023Text-to-3D using Gaussian Splattingcited by 299
  16. 2023Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifoldcited by 218
  17. 2023Scalable 3D Captioning with Pretrained Modelscited by 201
  18. 2022SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Viewscited by 175
  19. 2018Escaping Plato's Cave: 3D Shape From Adversarial Renderingcited by 170
  20. 20163D Shape Induction from 2D Views of Multiple Objectscited by 121
  21. 2020Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Renderingcited by 101
  22. 2017Improved Adversarial Systems for 3D Object Generation and Reconstructioncited by 66
  23. 2020Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Datacited by 30

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 8; 2026: 7.

21 papers.

Sort:

By area:

  1. 2024PrEditor3D: Fast and Precise 3D Shape Editingcited by 27
  2. 2025From One to More: Contextual Part Latents for 3D Generationcited by 26
  3. 2024Diffusion-Based Visual Art Creation: A Survey and New Perspectivescited by 17
  4. 2025FullPart: Generating each 3D Part at Full Resolutioncited by 17
  5. 2024MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priorscited by 12
  6. 2024GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generationcited by 12
  7. 2025Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimizationcited by 9
  8. 2025Towards Scalable and Consistent 3D Editingcited by 9
  9. 2024Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generationcited by 7
  10. 2025SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Trainingcited by 5
  11. 2025ScanEdit: Hierarchically-Guided Functional 3D Scan Editingcited by 4
  12. 2025Learning Heterogeneous Mixture of Scene Experts for Large-scale Neural Radiance Fieldscited by 4
9 more
  1. 20263DGS-Drag: Dragging Gaussians for Intuitive Point-Based 3D Editingcited by 4
  2. 2026Vinedresser3D: Agentic Text-guided 3D Editingcited by 3
  3. 2026TRACE: High-Fidelity 3D Scene Editing via Tangible Reconstruction and Geometry-Aligned Contextual Video Maskingcited by 2
  4. 2026Beyond Voxel 3D Editing: Learning from 3D Masks and Self-Constructed Datacited by 2
  5. 2024Editing Implicit and Explicit Representations of Radiance Fields: A Surveycited by 1
  6. 2026EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learningcited by 1
  7. 2025Dragen3D: Multiview Geometry Consistent 3D Gaussian Generation with Drag-Based Controlcited by 0
  8. 2026CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Controlcited by 0
  9. 2026Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editingcited by 0