[ECCV 2020] Unpaired Image-to-Image Translation using Adversarial Consistency Loss
Unpaired image-to-image translation is a class of vision problems whose goal is to find the mapping between different image domains using unpaired training data. Cycle-consistency loss is a widely used constraint for such problems. However, due to the strict pixel-level constraint, it cannot perform geometric changes, remove large objects, or ignore irrelevant texture. In this paper, we propose a novel adversarial-consistency loss for image-to-image translation. This loss does not require the translated image to be translated back to be a specific source image but can encourage the translated images to retain important features of the source images and overcome the drawbacks of cycle-consistency loss noted above. Our method achieves state-of-the-art results on three challenging tasks: glasses removal, male-to-female translation, and selfie-to-anime translation.
European Conference on Computer Vision (ECCV) is the top European conference in the image analysis area. ECCV, along with CVPR and ICCV, are regarded as the top conferences in the field of computer vision. ECCV is held biennially. Due to concerns about COVID-19, ECCV 2020 will be hosted online from August 23rd to August 28th, 2020.