Data-driven Motion Capture

Data-Driven Motion Capture is a motion capture methodology that leverages empirical motion data as the primary source for modeling, predicting, or reconstructing human movement, distinguished from model-based or rule-based approaches by its reliance on observed data patterns rather than predefined biomechanical constraints or explicit kinematic rules.

51

Publications

3.8K

Citations

147

Authors

68

Institutions

Publications per year

2017–2026

15

Authors

147

  1. JK
    Jessica K. Hodgins

    Carnegie Mellon University, United States

    4 publications · 213 citations

    4

    h-index

  2. MG
    Michael Gleicher

    University of Wisconsin–Madison, United States

    4 publications · 2K citations

    4

    h-index

  3. JZ
    Jianjun Zhang

    Bournemouth University, United Kingdom

    4 publications · 123 citations

    4

    h-index

  4. LK
    Lucas Kovar

    University of Wisconsin–Madison, United States

    4 publications · 2K citations

    4

    h-index

  5. XY
    Xiaosong Yang

    Bournemouth University, United Kingdom

    4 publications · 123 citations

    4

    h-index

1–5 of 147

Venues

  1. ACM Transactions on Graphics

    TOG · 4 publications · 932 citations

    4

    h-index

  2. Procedia CIRP

    2 publications · 42 citations

    2

    h-index

  3. 1

    h-index

  4. Behavior Research Methods

    BRM · 1 publications · 15 citations

    1

    h-index

  5. 1

    h-index

1–5