Haorui Ji, Taojun Lin, Hongdong Li
Diffusion models demonstrate remarkable capabilities in capturing complex data distributions and have achieved compelling results in many generative tasks. While they have recently been extended to dense prediction tasks such as depth estimation and surface normal prediction, their full potential in this area remains under-explored. In dense prediction settings, target signal maps and input images are pixel-wise aligned. This makes conventional noise-to-data generation paradigm inefficient, as input images can serve as more informative prior compared to pure noise. Diffusion bridge models, which support data-to-data generation between two general data distributions, offer a promising alternative, but they typically fail to exploit the rich visual priors embedded in large pretrained foundation models. To address these limitations, we integrate diffusion bridge formulation with structured visual priors and introduce DPBridge, the first latent diffusion bridge framework for dense prediction tasks. Our method presents three key contributions: (1) a tractable reverse transition kernel for diffusion bridge process, enabling maximum likelihood training scheme for better compatibility with pretrained backbones; (2) a distribution-aligned normalization technique to mitigate the discrepancies between the bridge and standard diffusion processes; and (3) an auxiliary image consistency loss to preserve fine-grained details. Experiments across extensive benchmarks validate that our method consistently achieves superior performance, demonstrating its effectiveness and generalization capability under different scenarios.