Autonomous Visual Agents

Autonomous Visual Agents is a computational framework in generative video art that integrates perceptual, cognitive, and motor components to enable an agent to independently interpret, decide, and act within visual environments, thereby generating dynamic visual content through self-directed interaction rather than scripted or externally controlled processes.

261

Publications

13.6K

Citations

887

Authors

263

Institutions

Publications per year

2017–2026

137

Authors

887

  1. SS
    Silvio Savarese

    Stanford University, United States

    9 publications · 1.2K citations

    9

    h-index

  2. DB
    Dhruv Batra

    Georgia Institute of Technology, United States

    8 publications · 621 citations

    8

    h-index

  3. MB
    Mohit Bansal

    University of North Carolina at Chapel Hill, United States

    6 publications · 415 citations

    6

    h-index

  4. FH
    Felix Hill

    University of Cambridge, United Kingdom

    6 publications · 189 citations

    6

    h-index

  5. YH
    Yicong Hong

    Australian National University, Australia

    6 publications · 301 citations

    6

    h-index

1–5 of 887

Venues

  1. arXiv (Cornell University)

    Repository · 61 publications · 2.4K citations

    25

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 491 citations

    5

    h-index

  3. 4

    h-index

  4. Computer Graphics Forum

    4 publications · 211 citations

    4

    h-index

  5. 4

    h-index

1–5