GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition

Xingyu Song; Zhan Li; Shi Chen; Kazuyuki Demachi

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arXiv · 2401.13414

GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition

Xingyu Song·Zhan Li·Shi Chen·Kazuyuki Demachi

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Abstract

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.

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BibTeX RIS

Xingyu Song, Zhan Li, Shi Chen, Kazuyuki Demachi. 2024-01-24. GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition. https://arxiv.org/abs/2401.13414

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