Data and Code for "Dancing through the Uncanny Valley: On the likeability of model-generated dance movements" - Leh, Amalaswintha; Justus Liebig University Giessen (My University, 2023)
Data and code for "Dancing through the Uncanny Valley: On the likeability of model-generated dance movements". Data includes Motion capture data (.BVH and .txt), electrodermal activity (EDA/EDASignals.csv) and likeability ratings. Code includes the Temporal movement primitive model (requires PyTorch; https://pytorch.org) and models for the likeability ratings (requires PyMC; https://www.pymc.io). Temporal movement primitives can be learned by providing Code/TMP_model.py the processed Joint angles (contained in the .bvh-files in the folder Data/MotionCapture). Models for the likeability ratings can be trained by providing Code/ModelComparisonRatingDiffs.py the data contained in Data/Ratings/Ratings.csv.
Keywords: ddc:150; ddc:770