Or Patashnik

2.8k total citations · 3 hit papers
22 papers, 1.1k citations indexed

About

Or Patashnik is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Computational Mechanics. According to data from OpenAlex, Or Patashnik has authored 22 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 4 papers in Computer Graphics and Computer-Aided Design and 2 papers in Computational Mechanics. Recurrent topics in Or Patashnik's work include Generative Adversarial Networks and Image Synthesis (15 papers), Image Retrieval and Classification Techniques (5 papers) and Computer Graphics and Visualization Techniques (4 papers). Or Patashnik is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (15 papers), Image Retrieval and Classification Techniques (5 papers) and Computer Graphics and Visualization Techniques (4 papers). Or Patashnik collaborates with scholars based in Israel, United States and Canada. Or Patashnik's co-authors include Daniel Cohen‐Or, Yuval Alaluf, Yotam Nitzan, Omer Tov, Rinon Gal, Amit H. Bermano, Haggai Maron, Gal Chechik, Elad Richardson and Raja Giryes and has published in prestigious journals such as ACM Transactions on Graphics, Computer Graphics Forum and Nature Human Behaviour.

In The Last Decade

Or Patashnik

21 papers receiving 1.0k citations

Hit Papers

Designing an encoder for StyleGAN image manipulation 2021 2026 2022 2024 2021 2022 2023 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Designing an encoder for StyleGAN image manipulation
    2021 389 citations Omer Tov, Yuval Alaluf et al. ACM Transactions on Graphics profile →
  2. StyleGAN-NADA
    2022 269 citations Rinon Gal, Or Patashnik et al. ACM Transactions on Graphics profile →
  3. Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
    2023 158 citations Gal Metzer, Elad Richardson et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Or Patashnik Israel 9 916 273 190 155 70 22 1.1k
Yuval Alaluf Israel 11 896 1.0× 221 0.8× 164 0.9× 154 1.0× 47 0.7× 14 1.1k
Omer Tov Israel 8 910 1.0× 197 0.7× 107 0.6× 179 1.2× 43 0.6× 12 1.1k
Rinon Gal Israel 11 672 0.7× 170 0.6× 84 0.4× 137 0.9× 44 0.6× 20 817
Ron Mokady Israel 9 700 0.8× 174 0.6× 97 0.5× 134 0.9× 33 0.5× 10 845
Zhouhui Lian China 20 1.1k 1.2× 368 1.3× 458 2.4× 92 0.6× 104 1.5× 83 1.4k
Pere‐Pau Vázquez Spain 15 633 0.7× 303 1.1× 134 0.7× 59 0.4× 36 0.5× 78 884
Yijun Li United States 16 1.2k 1.3× 203 0.7× 61 0.3× 190 1.2× 32 0.5× 42 1.4k
Haggai Maron Israel 7 441 0.5× 183 0.7× 280 1.5× 122 0.8× 24 0.3× 16 670
Cengiz Öztireli Switzerland 13 461 0.5× 180 0.7× 267 1.4× 177 1.1× 71 1.0× 23 737
Lele Chen United States 10 574 0.6× 117 0.4× 112 0.6× 67 0.4× 61 0.9× 31 745

Countries citing papers authored by Or Patashnik

Since Specialization
Citations

This map shows the geographic impact of Or Patashnik's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Or Patashnik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Or Patashnik more than expected).

Fields of papers citing papers by Or Patashnik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Or Patashnik. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Or Patashnik. The network helps show where Or Patashnik may publish in the future.

Co-authorship network of co-authors of Or Patashnik

This figure shows the co-authorship network connecting the top 25 collaborators of Or Patashnik. A scholar is included among the top collaborators of Or Patashnik based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Or Patashnik. Or Patashnik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Parmar, Gaurav, Or Patashnik, Srinivasa G. Narasimhan, et al.. (2025). Object-level Visual Prompts for Compositional Image Generation. 1–12. 1 indexed citations
2.
Patashnik, Or, et al.. (2025). Nested Attention: Semantic-aware Attention Values for Concept Personalization. 1–12. 2 indexed citations
3.
Qian, Guocheng, et al.. (2025). Omni-ID: Holistic Identity Representation Designed for Generative Tasks. 8786–8795. 2 indexed citations
4.
Avrahami, Omri, Or Patashnik, Ohad Fried, et al.. (2025). Stable Flow: Vital Layers for Training-Free Image Editing. 7877–7888. 1 indexed citations
5.
Patashnik, Or, Gaurav Parmar, Anyi Rao, et al.. (2025). AI for Creative Visual Content Generation, Editing and Understanding. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–2. 1 indexed citations
6.
Abdal, Rameen, Or Patashnik, Ivan Skorokhodov, et al.. (2025). Dynamic Concepts Personalization from Single Videos. 1–9. 2 indexed citations
7.
Gal, Rinon, et al.. (2024). TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models. 1–12. 4 indexed citations
8.
Patashnik, Or, et al.. (2024). Consolidating Attention Features for Multi-view Image Editing. 1–12. 2 indexed citations
9.
Li, Ming, et al.. (2024). CLIP-Flow: Decoding images encoded in CLIP space. Computational Visual Media. 10(6). 1157–1168.
10.
Patashnik, Or, et al.. (2024). Using deep neural networks to disentangle visual and semantic information in human perception and memory. Nature Human Behaviour. 8(4). 702–717. 8 indexed citations
11.
Patashnik, Or, et al.. (2024). CLiC: Concept Learning in Context. 6924–6933. 7 indexed citations
12.
Patashnik, Or, et al.. (2023). Localizing Object-level Shape Variations with Text-to-Image Diffusion Models. 22994–23004. 41 indexed citations
13.
Metzer, Gal, Elad Richardson, Or Patashnik, Raja Giryes, & Daniel Cohen‐Or. (2023). Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures. 12663–12673. 158 indexed citations breakdown →
14.
Bermano, Amit H., Rinon Gal, Yuval Alaluf, et al.. (2022). State‐of‐the‐Art in the Architecture, Methods and Applications of StyleGAN. Computer Graphics Forum. 41(2). 591–611. 52 indexed citations
15.
Patashnik, Or, et al.. (2022). StyleFusion: Disentangling Spatial Segments in StyleGAN-Generated Images. ACM Transactions on Graphics. 41(5). 1–15. 14 indexed citations
16.
Tov, Omer, Yuval Alaluf, Yotam Nitzan, Or Patashnik, & Daniel Cohen‐Or. (2021). Designing an encoder for StyleGAN image manipulation. ACM Transactions on Graphics. 40(4). 1–14. 389 indexed citations breakdown →
17.
Tov, Omer, Yuval Alaluf, Yotam Nitzan, Or Patashnik, & Daniel Cohen‐Or. (2021). Designing an encoder for StyleGAN image manipulation. ACM Transactions on Graphics. 40(4). 1–14. 22 indexed citations
18.
Alaluf, Yuval, Or Patashnik, & Daniel Cohen‐Or. (2021). Only a matter of style. ACM Transactions on Graphics. 40(4). 1–12. 78 indexed citations
19.
Patashnik, Or, et al.. (2021). BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains. 2653–2661. 6 indexed citations
20.
Patashnik, Or, Min Lü, Amit H. Bermano, & Daniel Cohen‐Or. (2020). Temporal scatterplots. Computational Visual Media. 6(4). 385–400. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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