[2603.12057] Coarse-Guided Visual Generation via Weighted h-Transform Sampling

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.12057 (cs) [Submitted on 12 Mar 2026 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Coarse-Guided Visual Generation via Weighted h-Transform Sampling Authors:Yanghao Wang, Ziqi Jiang, Zhen Wang, Long Chen View a PDF of the paper titled Coarse-Guided Visual Generation via Weighted h-Transform Sampling, by Yanghao Wang and 3 other authors View PDF HTML (experimental) Abstract:Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance during the sampling process. However, these training-free methods either require knowing the forward (fine-to-coarse) transformation operator, e.g., bicubic downsampling, or are difficult to balance between guidance and synthetic quality. To address these challenges, we propose a novel guided method by using the h-transform, a tool that can constrain stochastic processes (e.g., sampling process) under desired conditions. Specifically, we modify the transition probability at each sampling timestep by adding to the original differential equation with a drift function, which approximately steer...