Play and Learn: Using Video Games to Train Computer Vision Models

  title={Play and Learn: Using Video Games to Train Computer Vision Models},
  author={Alireza Shafaei and James J. Little and Mark Schmidt},
Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to improve the performance of computer vision models in practice. We present experiments assessing the effectiveness on real-world data of systems trained on synthetic RGB images that are extracted from a video game. We collected over 60,000 synthetic samples… CONTINUE READING
Highly Cited
This paper has 60 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 21 times over the past 90 days. VIEW TWEETS
38 Citations
53 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 38 extracted citations

61 Citations

Citations per Year
Semantic Scholar estimates that this publication has 61 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 53 references

Little . Real - time human motion capture with multiple depth cameras

  • Alireza Shafaei, J. James
  • 2016

Similar Papers

Loading similar papers…