  Contents generated by recent advanced Text-to-Image (T2I) diffusion models
are sometimes too imaginative for existing off-the-shelf dense predictors to
estimate due to the immitigable domain gap. We introduce DMP, a pipeline
utilizing pre-trained T2I models as a prior for dense prediction tasks. To
address the misalignment between deterministic prediction tasks and stochastic
T2I models, we reformulate the diffusion process through a sequence of
interpolations, establishing a deterministic mapping between input RGB images
and output prediction distributions. To preserve generalizability, we use
low-rank adaptation to fine-tune pre-trained models. Extensive experiments
across five tasks, including 3D property estimation, semantic segmentation, and
intrinsic image decomposition, showcase the efficacy of the proposed method.
Despite limited-domain training data, the approach yields faithful estimations
for arbitrary images, surpassing existing state-of-the-art algorithms.
