Consistent Feature Transport for Image Relighting

Image relighting modifies illumination while preserving non-lighting content such as identity and geometry. Existing diffusion-basedmethods often suffer from unstable illumination changes or inconsistentcontent preservation under complex lighting, as they lack an explicitmechanism to learn feature transformations between images. We refor-mulate relighting as an illumination feature transport problem and in-troduce Consistent Feature Transport (CFT), a training principle thatexplicitly enforces illumination-consistent transport between source andtarget image distributions. Built upon rectified flow, CFT jointly modelsnoise-to-image generation and illumination-consistent source-to-targettransport through trajectory-level supervision. This dual-transport for-mulation encourages isolation of illumination-specific variations whilepreserving content-aligned features. To support complex lighting scenar-ios, we construct a large-scale portrait relighting dataset with diverserelighting effects. Experiments show consistent improvements over ex-isting state-of-the-art relighting approaches and demonstrate that CFTcan generalize to other editing tasks, including style transfer. Code isavailable at https://github.com/Dixin-Lab/CFT.