GTAutoAct: An Automatic Datasets Generation Framework Based on Game
  Engine Redevelopment for Action Recognition
Xingyu Song Zhan Li Shi Chen Kazuyuki Demachi
Current datasets for action recognition tasks face limitations stemming from
traditional collection and generation methods, including the constrained range
of action classes, absence of multi-viewpoint recordings, limited diversity,
poor video quality, and labor-intensive manually collection. To address these
challenges, we introduce GTAutoAct, a innovative dataset generation framework
leveraging game engine technology to facilitate advancements in action
recognition. GTAutoAct excels in automatically creating large-scale,
well-annotated datasets with extensive action classes and superior video
quality. Our framework's distinctive contributions encompass: (1) it
innovatively transforms readily available coordinate-based 3D human motion into
rotation-orientated representation with enhanced suitability in multiple
viewpoints; (2) it employs dynamic segmentation and interpolation of rotation
sequences to create smooth and realistic animations of action; (3) it offers
extensively customizable animation scenes; (4) it implements an autonomous
video capture and processing pipeline, featuring a randomly navigating camera,
with auto-trimming and labeling functionalities. Experimental results
underscore the framework's robustness and highlights its potential to
significantly improve action recognition model training.