  We present a novel approach designed to address the complexities posed by
challenging, out-of-distribution data in the single-image depth estimation
task. Starting with images that facilitate depth prediction due to the absence
of unfavorable factors, we systematically generate new, user-defined scenes
with a comprehensive set of challenges and associated depth information. This
is achieved by leveraging cutting-edge text-to-image diffusion models with
depth-aware control, known for synthesizing high-quality image content from
textual prompts while preserving the coherence of 3D structure between
generated and source imagery. Subsequent fine-tuning of any monocular depth
network is carried out through a self-distillation protocol that takes into
account images generated using our strategy and its own depth predictions on
simple, unchallenging scenes. Experiments on benchmarks tailored for our
purposes demonstrate the effectiveness and versatility of our proposal.
