Geometric deep learning enables 3D kinematic profiling across species and environments

[1]  Timothy W. Dunn,et al.  Geometric deep learning enables 3D kinematic profiling across species and environments , 2021, Nature Methods.

[2]  Timothy W. Dunn,et al.  Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire , 2020, Neuron.

[3]  Seng Bum Michael Yoo,et al.  Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio , 2020, Nature Communications.

[4]  Matthew J. Johnson,et al.  Revealing the structure of pharmacobehavioral space through Motion Sequencing , 2020, Nature Neuroscience.

[5]  Bingni W. Brunton,et al.  Anipose: A toolkit for robust markerless 3D pose estimation , 2020, bioRxiv.

[6]  Pascal Fua,et al.  Lightweight Multi-View 3D Pose Estimation Through Camera-Disentangled Representation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Thomas Brox,et al.  FreiPose: A Deep Learning Framework for Precise Animal Motion Capture in 3D Spaces , 2020, bioRxiv.

[8]  Pascal Fua,et al.  DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila , 2019, eLife.

[9]  Scott W. Linderman,et al.  BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos , 2019, NeurIPS.

[10]  Wenjun Zeng,et al.  Cross View Fusion for 3D Human Pose Estimation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[11]  Andrew Zisserman,et al.  Sim2real transfer learning for 3D human pose estimation: motion to the rescue , 2019, NeurIPS.

[12]  A. Ayaz,et al.  Layer-specific integration of locomotion and sensory information in mouse barrel cortex , 2019, Nature Communications.

[13]  Gordon Wetzstein,et al.  Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations , 2019, NeurIPS.

[14]  Victor Lempitsky,et al.  Learnable Triangulation of Human Pose , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[15]  Jacob M. Graving,et al.  DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning , 2019, bioRxiv.

[16]  Junsong Yuan,et al.  3D Hand Shape and Pose Estimation From a Single RGB Image , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Stephen B. Dunnett,et al.  The Amphetamine Induced Rotation Test: A Re-Assessment of Its Use as a Tool to Monitor Motor Impairment and Functional Recovery in Rodent Models of Parkinson’s Disease , 2019, Journal of Parkinson's disease.

[18]  Dario Pavllo,et al.  3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Matthias Bethge,et al.  Using DeepLabCut for 3D markerless pose estimation across species and behaviors , 2018, Nature Protocols.

[20]  Lourdes Agapito,et al.  Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture , 2018, 2018 International Conference on 3D Vision (3DV).

[21]  Scott W. Linderman,et al.  The Striatum Organizes 3D Behavior via Moment-to-Moment Action Selection , 2018, Cell.

[22]  Talmo D. Pereira,et al.  Fast animal pose estimation using deep neural networks , 2018, bioRxiv.

[23]  Bartul Mimica,et al.  Efficient cortical coding of 3D posture in freely behaving rats , 2018, Science.

[24]  Matthias Bethge,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

[25]  Yichen Wei,et al.  Integral Human Pose Regression , 2017, ECCV.

[26]  Kyoung Mu Lee,et al.  V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Jitendra Malik,et al.  Learning a Multi-View Stereo Machine , 2017, NIPS.

[28]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[29]  Xiaowei Zhou,et al.  Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[31]  Bernt Schiele,et al.  DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model , 2016, ECCV.

[32]  David A. Leopold,et al.  Marmosets: A Neuroscientific Model of Human Social Behavior , 2016, Neuron.

[33]  Leonidas J. Guibas,et al.  Volumetric and Multi-view CNNs for Object Classification on 3D Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[35]  Ann M. Graybiel,et al.  Neurobiology of rodent self-grooming and its value for translational neuroscience , 2015, Nature Reviews Neuroscience.

[36]  Ryan P. Adams,et al.  Mapping Sub-Second Structure in Mouse Behavior , 2015, Neuron.

[37]  Takeo Kanade,et al.  Panoptic Studio: A Massively Multiview System for Social Motion Capture , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[38]  David J. Anderson,et al.  Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning , 2015, Proceedings of the National Academy of Sciences.

[39]  João Fayad,et al.  A quantitative framework for whole-body coordination reveals specific deficits in freely walking ataxic mice , 2015, eLife.

[40]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[41]  Alexander Ferrein,et al.  IR Stereo Kinect: Improving Depth Images by Combining Structured Light with IR Stereo , 2014, PRICAI.

[42]  Pietro Perona,et al.  Automated image-based tracking and its application in ecology. , 2014, Trends in ecology & evolution.

[43]  Cristian Sminchisescu,et al.  Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Bernt Schiele,et al.  2D Human Pose Estimation: New Benchmark and State of the Art Analysis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Zengcai V. Guo,et al.  Flow of Cortical Activity Underlying a Tactile Decision in Mice , 2014, Neuron.

[46]  William Bialek,et al.  Mapping the stereotyped behaviour of freely moving fruit flies , 2013, Journal of The Royal Society Interface.

[47]  Christopher D. Harvey,et al.  Choice-specific sequences in parietal cortex during a virtual-navigation decision task , 2012, Nature.

[48]  N. Tinbergen.  On aims and methods of Ethology , 2010 .

[49]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[50]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion , 2010, International Journal of Computer Vision.

[51]  Greg J. Stephens,et al.  Dimensionality and Dynamics in the Behavior of C. elegans , 2007, PLoS Comput. Biol..

[52]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[53]  A. Berthoz,et al.  Head stabilization during various locomotor tasks in humans , 1990, Experimental Brain Research.

[54]  J C Fentress,et al.  Early ontogeny of face grooming in mice. , 1985, Developmental psychobiology.

[55]  P. Marler,et al.  Developmental overproduction and selective attrition: new processes in the epigenesis of birdsong. , 1982, Developmental psychobiology.

[56]  R. Andrew,et al.  Precocious adult behaviour in the young chick. , 1966, Animal behaviour.

[57]  R. Bolles,et al.  The ontogeny of behaviour in the albino rat , 1964 .

[58]  A. Berthoz,et al.  Head stabilization during various locomotor tasks in humans , 2004, Experimental Brain Research.

[59]  H. Opower.  Multiple view geometry in computer vision , 2002 .

[60]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .