Hierarchy Flow For High-Fidelity Image-to-Image Translation
Weichen Fan Jinghuan Chen Ziwei Liu
Image-to-image (I2I) translation comprises a wide spectrum of tasks. Here we
divide this problem into three levels: strong-fidelity translation,
normal-fidelity translation, and weak-fidelity translation, indicating the
extent to which the content of the original image is preserved. Although
existing methods achieve good performance in weak-fidelity translation, they
fail to fully preserve the content in both strong- and normal-fidelity tasks,
e.g. sim2real, style transfer and low-level vision. In this work, we propose
Hierarchy Flow, a novel flow-based model to achieve better content preservation
during translation. Specifically, 1) we first unveil the drawbacks of standard
flow-based models when applied to I2I translation. 2) Next, we propose a new
design, namely hierarchical coupling for reversible feature transformation and
multi-scale modeling, to constitute Hierarchy Flow. 3) Finally, we present a
dedicated aligned-style loss for a better trade-off between content
preservation and stylization during translation. Extensive experiments on a
wide range of I2I translation benchmarks demonstrate that our approach achieves
state-of-the-art performance, with convincing advantages in both strong- and
normal-fidelity tasks. Code and models will be at
https://github.com/WeichenFan/HierarchyFlow.