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LDM3D-VR: Latent Diffusion Model for 3D VR

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arxiv 2311.03226 v1 pith:KLOSMWVU submitted 2023-11-06 cs.CV cs.AI

classification cs.CVcs.AI
keywords modelsdiffusiondepthexistinggenerationhigh-resolutionlatentldm3d-vr
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DreamCube: 3D Panorama Generation via Multi-plane Synchronization

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A synchronized multi-plane adaptation of 2D diffusion operators enables seam-consistent cubemap generation, and DreamCube extends this to joint RGB-D panorama generation and 3D scene lifting.

  2. Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Vid-CamEdit re-synthesizes monocular videos along user-defined camera paths by conditioning a video diffusion model on 2D flows derived from estimated 3D geometry, without training on multi-view video data.

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