3D Hand Pose Estimation from Single RGB Camera

Chernytska, Olha

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Факультет прикладних наук
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Освітня програма наук про дані
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2019
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dc.contributor.author	Chernytska, Olha
dc.date.accessioned	2019-02-18T13:43:12Z
dc.date.available	2019-02-18T13:43:12Z
dc.date.issued	2019
dc.identifier.citation	Chernytska, Olha. 3D Hand Pose Estimation from Single RGB Camera : Master Thesis : manuscript / Olha Chernytska ; Supervisor Dmitry Pranchuk ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2019. – 43 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/1327
dc.language.iso	en	uk
dc.subject	3D hand pose estimation	uk
dc.subject	RGB Camera	uk
dc.subject	Datasets	uk
dc.title	3D Hand Pose Estimation from Single RGB Camera	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	With the increase of popularity VR/AR applications, 3D hand pose estimation task has become very popular. 3D hand pose estimation from single RGB camera has great potential, because RGB cameras are cheap and already available on most mobile devices. In this thesis we work on improving pipeline for 3D hand pose estimation from RGB camera. We dealt with two challenges - sophisticated algorithmic task and absence of good datasets. We trained several convolutional neural networks and showed that direct heatmaps method is the best approach for 2D pose estimation and vector representation - for 3D pose. We demonstrated that adding data augmentations even for synthetic dataset increases performance on real data. For 2D hand pose estimation, we proved that it is possible to train neural network on large scale synthetic dataset and finetune it on small partly labeled real dataset to receive adequate results, even when only small part of keypoint labels is available. With no real 3D labels available, model trained on synthetic data still could correctly predict 3D keypoint locations for simple poses. All code and pre-trained models will be publicly available.	uk