472 citations · 907 across the 31 of their papers we have counts for
9 papers · 1 filter
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators
Dvir Yerushalmi, Dov Danon, Amit H. Bermano
Supervision for image-to-image translation (I2I) tasks is hard to come by, but bears significant effect on the resulting quality. In this paper, we observe that for many Unsupervis…
Learned Queries for Efficient Local Attention
Moab Arar, Ariel Shamir, Amit H. Bermano
Vision Transformers (ViT) serve as powerful vision models. Unlike convolutional neural networks, which dominated vision research in previous years, vision transformers enjoy the ab…
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
Yuval Alaluf, Omer Tov, Ron Mokady +2
The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, d…
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Rinon Gal, Or Patashnik, Haggai Maron +2
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator be traine…
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
Ron Mokady, Rotem Tzaban, Sagie Benaim +2
The task of unsupervised motion retargeting in videos has seen substantial advancements through the use of deep neural networks. While early works concentrated on specific object p…
Pivotal Tuning for Latent-based Editing of Real Images
Daniel Roich, Ron Mokady, Amit H. Bermano +1
Recently, a surge of advanced facial editing techniques have been proposed that leverage the generative power of a pre-trained StyleGAN. To successfully edit an image this way, one…
MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation
Amir Barda, Yotam Erel, Amit H. Bermano
Mesh-based learning is one of the popular approaches nowadays to learn shapes. The most established backbone in this field is MeshCNN. In this paper, we propose infusing MeshCNN wi…
InAugment: Improving Classifiers via Internal Augmentation
Moab Arar, Ariel Shamir, Amit Bermano
Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image. These augmentations were proven useful in improving…
SWAGAN: A Style-based Wavelet-driven Generative Model
Rinon Gal, Dana Cohen, Amit Bermano +1
In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs). Even so, these networks still suffer from degradation in quali…