  Scene depth estimation from paintings can streamline the process of 3D
sculpture creation so that visually impaired people appreciate the paintings
with tactile sense. However, measuring depth of oriental landscape painting
images is extremely challenging due to its unique method of depicting depth and
poor preservation. To address the problem of scene depth estimation from
oriental landscape painting images, we propose a novel framework that consists
of two-step Image-to-Image translation method with CLIP-based image matching at
the front end to predict the real scene image that best matches with the given
oriental landscape painting image. Then, we employ a pre-trained SOTA depth
estimation model for the generated real scene image. In the first step,
CycleGAN converts an oriental landscape painting image into a pseudo-real scene
image. We utilize CLIP to semantically match landscape photo images with an
oriental landscape painting image for training CycleGAN in an unsupervised
manner. Then, the pseudo-real scene image and oriental landscape painting image
are fed into DiffuseIT to predict a final real scene image in the second step.
Finally, we measure depth of the generated real scene image using a pre-trained
depth estimation model such as MiDaS. Experimental results show that our
approach performs well enough to predict real scene images corresponding to
oriental landscape painting images. To the best of our knowledge, this is the
first study to measure the depth of oriental landscape painting images. Our
research potentially assists visually impaired people in experiencing paintings
in diverse ways. We will release our code and resulting dataset.
