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Learning Human Optical Flow


Conference Paper


The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.

Author(s): Anurag Ranjan and Javier Romero and Michael J. Black
Book Title: 29th British Machine Vision Conference
Year: 2018
Month: September

Department(s): Perceiving Systems
Research Project(s): Learning from Synthetic Data
Optical Flow and Human Action
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Place: Newcastle upon Tyne

URL: https://github.com/anuragranj/humanflow

Links: video
Attachments: pdf


  title = {Learning Human Optical Flow},
  author = {Ranjan, Anurag and Romero, Javier and Black, Michael J.},
  booktitle = { 29th British Machine Vision Conference},
  month = sep,
  year = {2018},
  url = {https://github.com/anuragranj/humanflow},
  month_numeric = {9}