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.
LDM3D-VR: Latent Diffusion Model for 3D VR
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abstract
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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DreamCube: 3D Panorama Generation via Multi-plane Synchronization
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.