Interactive Image Synthesis

Interactive Image Synthesis is a generative visual modeling framework that enables real-time, bidirectional user-system collaboration to iteratively refine image outputs through structured feedback, distinguishing itself from passive or fully autonomous synthesis methods by embedding human intent into the generation loop.

102

Publications

18.4K

Citations

339

Authors

123

Institutions

Publications per year

2017–2026

63

Authors

339

  1. JZ
    Jun-Yan Zhu

    Carnegie Mellon University, United States

    7 publications · 5.2K citations

    7

    h-index

  2. JK
    Jan Kautz

    University College London, United Kingdom

    5 publications · 4.9K citations

    5

    h-index

  3. ES
    Eli Shechtman

    Adobe Systems (United States), United States

    4 publications · 316 citations

    4

    h-index

  4. RZ
    Richard Zhang

    Adobe Systems (United States), United States

    3 publications · 217 citations

    3

    h-index

  5. JL
    Jingwan Lu

    Adobe Systems (United States), United States

    2 publications · 404 citations

    2

    h-index

1–5 of 339

Venues

  1. ACM Transactions on Graphics

    TOG · 11 publications · 1.7K citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 13 publications · 671 citations

    11

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 98 citations

    3

    h-index

  4. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 151 citations

    3

    h-index

  5. IEEE Computer Graphics and Applications

    3 publications · 336 citations

    3

    h-index

1–5