RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects
Sascha Kirch Valeria Olyunina Jan Ondřej Rafael Pagés Sergio Martin Clara Pérez-Molina
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.