Image Warping for Image-to-Image Translation

Image-to-image (I2I) translation has achieved strong resultsin tasks like human relighting and driving scene translation using la-tent diffusion models (LDMs). However, compact LDMs often strug-gle to preserve fine-grained structures because the encoder compresseshigh-resolution inputs into a spatially downsampled latent space. Toaddress this issue, we propose a simple saliency-guided warp–unwarpframework that reallocates spatial representation toward salient regionsbefore encoding, enabling better preservation of structural details with-out increasing latent resolution. The warped image is processed by theoriginal diffusion model and then mapped back via an inverse warp.In addition, we propose a simple and efficient outpainting-based syn-thetic data generation pipeline to produce high-quality paired data forimage relighting. Our method is model-agnostic, requires no architec-tural modification, and introduces negligible computational overhead.Experiments on human relighting, driving scene relighting, and transla-tion demonstrate improved structural preservation, lighting faithfulness,and image quality, with our framework extending naturally to video viaframe-by-frame application with good temporal stability. Project Web-page: https://shenzheng2000.github.io/WarpI2I.github.io/