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@Article{YurtogluSDG2024,
	author	=	{Ayda Yurto{\^g}lu and Sinan Sonlu and Yal{\i}m Do{\^g}an and U{\^g}ur G{\"u}d{\"u}kbay},
	title = {Personality Perception in Human Videos Altered by Motion Transfer Networks},
	journal = {Computers \& Graphics},
	volume = {119},
	pages = {Article No. 103886, 11 pages},
	year = {2024},
	month	= {April},
	issn = {0097-8493},
	doi = {https://doi.org/10.1016/j.cag.2024.01.013},
	url = {https://www.sciencedirect.com/science/article/pii/S009784932400013X},
	keywords = {Pedestrian detection and tracking, Data-driven simulation, Three-dimensional reconstruction, 
	            Crowd simulation, Augmented reality, Deep learning},
	abstract = {Crowd simulations imitate the group dynamics of individuals in different environments. 
	            Applications in entertainment, security, and education require augmenting simulated 
	            crowds into videos of real people. In such cases, virtual agents should realistically 
	            interact with the environment and the people in the video. One component of this 
	            augmentation task is determining the navigable regions in the video. In this work, 
	            we utilize semantic segmentation and pedestrian detection to automatically locate and 
	            reconstruct the navigable regions of surveillance-like videos. We place the resulting 
	            flat mesh into our 3D crowd simulation environment to integrate virtual agents that 
	            navigate inside the video avoiding collision with real pedestrians and other virtual 
	            agents. We report the performance of our open-source system using real-life surveillance 
	            videos, based on the accuracy of the automatically determined navigable regions and 
	            camera configuration. We show that our system generates accurate navigable regions for 
	            realistic augmented crowd simulations.}
	bib2html_dl_pdf = {http://www.cs.bilkent.edu.tr/~gudukbay/publications/papers/journal_articles/Yurtoglu_Et_Al_CAG_2024.pdf},
	bib2html_pubtype = {Refereed Journal Articles},
	bib2html_rescat = {Computer Graphics}
}