Generative Image Synthesis

Generative Image Synthesis is a computational method that belongs to the class of generative models in artificial intelligence, distinguished by its capacity to autonomously produce novel visual content through learned data distributions rather than manual design or direct sampling.

2.1K

Publications

214.1K

Citations

6.4K

Authors

1.4K

Institutions

Publications per year

2017–2026

2K

Authors

6.4K

  1. JZ
    Jun-Yan Zhu

    Carnegie Mellon University, United States

    28 publications · 10.8K citations

    22

    h-index

  2. HT
    Hao Tang

    University of Trento, Italy

    22 publications · 1.6K citations

    19

    h-index

  3. DC
    Daniel Cohen‐Or

    Tel Aviv University, Israel

    24 publications · 2.9K citations

    19

    h-index

  4. YS
    Yujun Shen

    Chinese University of Hong Kong, Hong Kong SAR China

    20 publications · 3K citations

    18

    h-index

  5. NS
    Nicu Sebe

    University of Trento, Italy

    21 publications · 1.2K citations

    18

    h-index

1–5 of 6.4K

Venues

  1. arXiv (Cornell University)

    Repository · 534 publications · 61.4K citations

    91

    h-index

  2. 31

    h-index

  3. ACM Transactions on Graphics

    TOG · 35 publications · 5.5K citations

    29

    h-index

  4. IEEE Access

    54 publications · 2.2K citations

    26

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 39 publications · 4.2K citations

    25

    h-index

1–5