Papers › Guided Image-to-Image Translation with Bi-Directional Feature Transformation

Guided Image-to-Image Translation with Bi-Directional Feature Transformation

24 Oct 2019ICCV 2019 10arXiv:1910.11328archive 2025-07-28

Badour AlBahar, Jia-Bin Huang

We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image. Various conditioning methods for leveraging the given guidance image have been explored, including input concatenation , feature concatenation, and conditional affine transformation of feature activations. All these conditioning mechanisms, however, are uni-directional, i.e., no information flow from the input image back to the guidance. To better utilize the constraints of the guidance image, we present a bi-directional feature transformation (bFT) scheme. We show that our bFT scheme outperforms other conditioning schemes and has comparable results to state-of-the-art methods on different tasks.

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