Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 36; číslo 7; s. 1325 - 1339
Hlavní autoři:	Ionescu, Catalin, Papava, Dragos, Olaru, Vlad, Sminchisescu, Cristian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.07.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Artificial intelligence Biometry - methods Cameras Computer science; control theory; systems Computer systems and distributed systems. User interface Databases, Factual Ecosystem Estimation Exact sciences and technology Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Information systems. Data bases Joints Learning and adaptive systems Matematik Mathematical Sciences Memory organisation. Data processing Modeling and recovery of physical attributes Motion Natural Sciences Naturvetenskap Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Photography - methods Posture Reproducibility of Results Sensitivity and Specificity Sensors Software Solid modeling Subtraction Technique Three-dimensional displays Training Whole Body Imaging - methods large-scale learning structured prediction articulated body modeling optimization human motion capture data 3D human pose estimation Fourier kernel approximations Occlusion Very large databases Modeling Optimization Posture Natural environment Scientific research Classification Internal structure Data structure Learning algorithm Computer vision Statistical analysis Motion estimation Viewing angle Computer animation Augmented reality Educational software program Human activity Time of flight method Mixed reality Data visualization Body movement Large scale Occultation Structural analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.
AbstractList	We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m. We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m. We introduce a new dataset, Human3.6M, of 3.6 Million 3D Human poses, acquired by recording the performance of 11 subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models. Besides increasing the size the current state of the art datasets by several orders of magnitude, we aim to complement such datasets with a diverse set of poses encountered in typical human activities (taking photos, posing, greeting, eating, etc.), with synchronized image, motion capture and depth data, and with accurate 3D body scans of all subjects involved. We also provide mixed reality videos where 3D human models are animated using motion capture data and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide large scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future workin the research community. The dataset and code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, are available online at http://vision.imar.ro/human3.6m.
Author	Olaru, Vlad Ionescu, Catalin Papava, Dragos Sminchisescu, Cristian
Author_xml	– sequence: 1 givenname: Catalin surname: Ionescu fullname: Ionescu, Catalin email: catalin.ionescu@ins.uni-bonn.de organization: Inst. of Math., Bucharest, Romania – sequence: 2 givenname: Dragos surname: Papava fullname: Papava, Dragos email: dragos.papava@imar.ro organization: Inst. of Math., Bucharest, Romania – sequence: 3 givenname: Vlad surname: Olaru fullname: Olaru, Vlad email: vlad.olaru@imar.ro organization: Inst. of Math., Bucharest, Romania – sequence: 4 givenname: Cristian surname: Sminchisescu fullname: Sminchisescu, Cristian email: cristian.sminchisescu@math.lth.se organization: Dept. of Math., Lund Univ., Lund, Sweden
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28603589$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/26353306$$D View this record in MEDLINE/PubMed
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Keywords	large-scale learning structured prediction articulated body modeling optimization human motion capture data 3D human pose estimation Fourier kernel approximations Occlusion Very large databases Modeling Optimization Posture Natural environment Scientific research Classification Internal structure Data structure Learning algorithm Computer vision Statistical analysis Motion estimation Viewing angle Computer animation Augmented reality Educational software program Human activity Time of flight method Mixed reality Data visualization Body movement Large scale Occultation Structural analysis
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Snippet	We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4... We introduce a new dataset, Human3.6M, of 3.6 Million 3D Human poses, acquired by recording the performance of 11 subjects, under 4 different viewpoints, for...
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SubjectTerms	Algorithms Applied sciences Artificial intelligence Biometry - methods Cameras Computer science; control theory; systems Computer systems and distributed systems. User interface Databases, Factual Ecosystem Estimation Exact sciences and technology Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Information systems. Data bases Joints Learning and adaptive systems Matematik Mathematical Sciences Memory organisation. Data processing Modeling and recovery of physical attributes Motion Natural Sciences Naturvetenskap Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Photography - methods Posture Reproducibility of Results Sensitivity and Specificity Sensors Software Solid modeling Subtraction Technique Three-dimensional displays Training Whole Body Imaging - methods
Title	Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
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