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RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects

2023-07-29Code Available0· sign in to hype

Sascha Kirch, Valeria Olyunina, Jan Ondřej, Rafael Pagés, Sergio Martin, Clara Pérez-Molina

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Abstract

We present RGB-D-Fusion, a multi-modal conditional denoising diffusion probabilistic model to generate high resolution depth maps from low-resolution monocular RGB images of humanoid subjects. RGB-D-Fusion first generates a low-resolution depth map using an image conditioned denoising diffusion probabilistic model and then upsamples the depth map using a second denoising diffusion probabilistic model conditioned on a low-resolution RGB-D image. We further introduce a novel augmentation technique, depth noise augmentation, to increase the robustness of our super-resolution model.

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