Subject: Generating Synthetic Data for Human Action Recognition
Hello,

In my master's thesis, I generated a realistic dataset that
can be used for human action recognition (using the Unity engine). The dataset
contains 2D - 3D pose information and RGB videos. I wanted to test the effects
of this dataset on real-world action detection (directly on videosYouTube) when
the classifier is trained with synthetic data in addition to real-data (NTU
120).
I want to use skeleton-based action recognition methodology
(since it outperforms RGB-only methodologies for NTU 120) and to achieve this I
applied a pose estimator to videos from YouTube, our synthetic dataset, and
NTU120 and trained them since I believe instead of using directly sterile
ground truth information of our dataset, I can apply pose estimator and use
those pose informations directly instead of worrying with domain adaptation
strategies.
Question is: Should I have directly used ground truth pose
information of our synthetic data in trainings with real-data, or the thing I
did does make sense? If there is any usage of pose estimators as domain
adaptation methods, I would be extremely happy if you can share the papers when
commenting.
Best,