mlsanityData to 14 Oct 2026

MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance

arXiv 2501.13449 ↗First posted 23 Jan 2025cs.CVcited by 1

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Abstract (arXiv)

While single-concept customization has been studied in 3D, multi-concept customization remains largely unexplored. To address this, we propose MultiDreamer3D that can generate coherent multi-concept 3D content in a divide-and-conquer manner. First, we generate 3D bounding boxes using an LLM-based layout controller. Next, a selective point cloud generator creates coarse point clouds for each concept. These point clouds are placed in the 3D bounding boxes and initialized into 3D Gaussian Splatting with concept labels, enabling precise identification of concept attributions in 2D projections. Finally, we refine 3D Gaussians via concept-aware interval score matching, guided by concept-aware diffusion. Our experimental results show that MultiDreamer3D not only ensures object presence and preserves the distinct identities of each concept but also successfully handles complex cases such as property change or interaction. To the best of our knowledge, we are the first to address the multi-concept customization in 3D.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 17 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

14 of 17 references.

References · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  7. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  8. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  9. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  10. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  11. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
  12. 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 385
2 more
  1. 2023LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matchingcited by 290
  2. 2023Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Modelscited by 261

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Citing papers per year: 2026: 1.

1 paper.