Improving Image-to-Image Translation via a Rectified Flow Reformulation
In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regression networks as continuous-time transport models. While pixel-wise I2I regression is simple, stable, and easy to adapt across tasks, it often over-smooths ill-posed and multimodal targets, whereas generative alternatives often require additional components, task-specific tuning, and more complex training and inference pipelines. Our method augments the backbone input by channel-wise concatenation with a noise-corrupted version of the ground-truth target and optimizes a simple t-reweighted pixel loss. This objective admits a rectified-flow inter...
| Authors | Satoshi Iizuka, Shun Okamoto, Kazuhiro Fukui |
|---|---|
| Primary category | cs.CV |
| Published | 20 March 2026 |
| Last updated at source | 31 August 2026 |
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