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Reconstructing Three-Dimensional Models of Interacting Humans

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Titel: Reconstructing Three-Dimensional Models of Interacting Humans
Autoren: Fieraru, Mihai, Zanfir, Mihai, Oneata, Elisabeta, Popa, Alin Ionut, Olaru, Vlad, Sminchisescu, Cristian
Weitere Verfasser: Lund University, Faculty of Science, Centre for Mathematical Sciences, Research groups at the Centre for Mathematical Sciences, Mathematical Imaging Group, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Forskargrupper vid Matematikcentrum, Mathematical Imaging Group, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematics (Faculty of Engineering), Computer Vision and Machine Learning, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematik LTH, Datorseende och maskininlärning, Originator
Quelle: IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(12):10870-10881
Schlagwörter: Natural Sciences, Computer and Information Sciences, Computer graphics and computer vision, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datorgrafik och datorseende
Beschreibung: Understanding 3D human interactions is fundamental for fine-grained scene analysis and behavioural modeling. However, most of the existing models predict incorrect, lifeless 3D estimates, that miss the subtle human contact aspects–the essence of the event–and are of little use for detailed behavioral understanding. This paper addresses such issues with several contributions: (1) we introduce models for interaction signature estimation (ISP) encompassing contact detection, segmentation, and 3D contact signature prediction; (2) we show how such components can be leveraged to ensure contact consistency during 3D reconstruction; (3) we construct several large datasets for learning and evaluating 3D contact prediction and reconstruction methods; specifically, we introduce CHI3D, a lab-based accurate 3D motion capture dataset with 631 sequences containing 2,525 contact events, 728,664 ground truth 3D poses, as well as FlickrCI3D, a dataset of 11,216 images, with 14,081 processed pairs of people, and 81,233 facet-level surface correspondences. Finally, (4) we propose methodology for recovering the ground-truth pose and shape of interacting people in a controlled setup and (5) annotate all 3D interaction motions in CHI3D with textual descriptions.
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  Data: Understanding 3D human interactions is fundamental for fine-grained scene analysis and behavioural modeling. However, most of the existing models predict incorrect, lifeless 3D estimates, that miss the subtle human contact aspects–the essence of the event–and are of little use for detailed behavioral understanding. This paper addresses such issues with several contributions: (1) we introduce models for interaction signature estimation (ISP) encompassing contact detection, segmentation, and 3D contact signature prediction; (2) we show how such components can be leveraged to ensure contact consistency during 3D reconstruction; (3) we construct several large datasets for learning and evaluating 3D contact prediction and reconstruction methods; specifically, we introduce CHI3D, a lab-based accurate 3D motion capture dataset with 631 sequences containing 2,525 contact events, 728,664 ground truth 3D poses, as well as FlickrCI3D, a dataset of 11,216 images, with 14,081 processed pairs of people, and 81,233 facet-level surface correspondences. Finally, (4) we propose methodology for recovering the ground-truth pose and shape of interacting people in a controlled setup and (5) annotate all 3D interaction motions in CHI3D with textual descriptions.
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