mlsanityData to 14 Oct 2026

LDM3D-VR: Latent Diffusion Model for 3D VR

arXiv 2311.03226 ↗First posted 6 Nov 2023cs.CV cs.AIcited by 8

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 33 of its 46 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

33 of 46 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 1.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2023LDM3D: Latent Diffusion Model for 3D
  2. 2023NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
  3. 2025ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models
  4. 2025HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation
  5. 2024Diffusion Models in 3D Vision: A Survey
  6. 2025SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
  7. 2024V3D: Video Diffusion Models are Effective 3D Generators
  8. 2026Dual Diffusion Models for Multi-modal Guided 3D Avatar Generation