NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions

Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that c...

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Bibliographische Detailangaben

Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 8834 - 8845
Hauptverfasser:	Zhang, Juze, Luo, Haimin, Yang, Hongdi, Xu, Xinru, Wu, Qianyang, Shi, Ye, Yu, Jingyi, Xu, Lan, Wang, Jingya
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2023
Schlagworte:	3D from multi-view and sensors Computer vision Geometry Pipelines Rendering (computer graphics) Shape Training Visualization
ISSN:	1063-6919
Online-Zugang:	Volltext
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