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20212023
most citedAn Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

472 citations · 487 across the 5 of their papers we have counts for

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cs.CV20233 cited

Cross-domain Compositing with Pretrained Diffusion Models

Roy Hachnochi, Mingrui Zhao, Nadav Orzech +4

Diffusion models have enabled high-quality, conditional image editing capabilities. We propose to expand their arsenal, and demonstrate that off-the-shelf diffusion models can be u…

cs.CV202312 cited

Single Motion Diffusion

Sigal Raab, Inbal Leibovitch, Guy Tevet +3

Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging do…

cs.CV2022472 cited

An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Rinon Gal, Yuval Alaluf, Yuval Atzmon +4

Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific uni…

cs.CV2021

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…

cs.CV2021

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…