ECCV 2026｜光照改了，人却变了？美图影像研究院提出一致特征传输重打光新方案CFT
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TL;DR - Meitu’s MT Lab introduced Consistent Feature Transport (CFT), an ECCV 2026-accepted method that reframes portrait relighting as explicit lighting-feature transport within a Rectified Flow model. It improves lighting quality and physical consistency while better preserving identity, geometry, and scene content.
- CFT jointly learns noise-to-target generation, noise-to-source reconstruction, and direct source-to-target lighting transfer; the latter uses Rectified Flow’s linear structure and a parallelogram construction.
- Its core transfer objective is supervised with images that have different content but share the same lighting transformation, helping disentangle reusable lighting changes from identity and scene differences.
- The team built a large portrait-relighting dataset spanning indoor and outdoor scenes and 14 lighting categories, including structured illumination, mixed color temperatures, and complex shadows.
- The full CFT configuration achieved the best reported ablation results across SSIM, PSNR, LPIPS, and FID, generalized to style-transfer models, and has been deployed in Picchi, AirBrush, and BeautyPlus.
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ECCV 2026｜光照改了，人却变了？美图影像研究院提出一致特征传输重打光新方案CFT
Public signals
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TL;DR - Meitu’s MT Lab introduced Consistent Feature Transport (CFT), an ECCV 2026-accepted method that reframes portrait relighting as explicit lighting-feature transport within a Rectified Flow model. It improves lighting quality and physical consistency while better preserving identity, geometry, and scene content.
- CFT jointly learns noise-to-target generation, noise-to-source reconstruction, and direct source-to-target lighting transfer; the latter uses Rectified Flow’s linear structure and a parallelogram construction.
- Its core transfer objective is supervised with images that have different content but share the same lighting transformation, helping disentangle reusable lighting changes from identity and scene differences.
- The team built a large portrait-relighting dataset spanning indoor and outdoor scenes and 14 lighting categories, including structured illumination, mixed color temperatures, and complex shadows.
- The full CFT configuration achieved the best reported ablation results across SSIM, PSNR, LPIPS, and FID, generalized to style-transfer models, and has been deployed in Picchi, AirBrush, and BeautyPlus.