Shlomi Fruchter
Research Subjects
Shlomi Fruchter has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Amir Hertz
Google, New-York, USA
2 shared publications - Daniel Cohen-Or
Tel Aviv University
2 shared publications - Moab Arar
Tel Aviv University
2 shared publications - Omri Avrahami
The Hebrew University of Jerusalem, Jerusalem, Israel
2 shared publications - Andrey Voynov
Moscow State University
1 shared publication - Ariel Shamir
Reichman University, Herzliya, Israel
1 shared publication
Latest Publications
- Open Access
- 3 December 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-11
PALP: Prompt Aligned Personalization of Text-to-Image Models
- 3 December 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-11
Open AccessPALP: Prompt Aligned Personalization of Text-to-Image Models
- Open Access
- 13 July 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-12
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of consistent characters, a crucial aspect for numerous real-world applications such as story visualization, game development, asset design, advertising, and more. Current methods typically rely on multiple pre-existing images of the target character or involve labor-intensive manual processes. In this work, we propose a fully automated solution for consistent character generation, with the sole input being a text prompt. We introduce an iterative procedure that, at each stage, identifies a coherent set of images sharing a similar identity and extracts a more consistent identity from this set. Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study. To conclude, we showcase several practical applications of our approach.
- 13 July 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-12
Open AccessThe Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of consistent characters, a crucial aspect for numerous real-world applications such as story visualization, game development, asset design, advertising, and more. Current methods typically rely on multiple pre-existing images of the target character or involve labor-intensive manual processes. In this work, we propose a fully automated solution for consistent character generation, with the sole input being a text prompt. We introduce an iterative procedure that, at each stage, identifies a coherent set of images sharing a similar identity and extracts a more consistent identity from this set. Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study. To conclude, we showcase several practical applications of our approach.