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Real-Time Human Pose Recognition in Parts from Single Depth Images Jamie Shotton Andrew Fitzgibbon Mat Cook Toby Sharp Mark Finocchi Richard Moore Alex Kipman Andrew Blake Microsoft Research Cambridge & Xbox Incubation CVPR 2011 Best Paper

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OUTLINE Introduction Data Body Part Inference and Joint Proposals Experiments Discussion

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Introduction Robust interactive human body tracking – gaming, human-computer interaction, security, – telepresence, health-care Real time depth cameras – tracking from frame to frame but struggle to re-initialize quickly and so are not robust – Our focus on per-frame initialization + tracking algorithm focus on pose recognition in parts – 3D position candidates for each skeletal joint

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Introduction appropriate tracking algorithm – Tracking people with twists and exponential maps (CVPR 1998) – Tracking loose limbed people (CVPR 2004) – Nonlinear body pose estimation from depth images (DAGM 2005) – Real-time hand-tracking with a color glove (ACM 2009) – Real time motion capture using a single time-of-flight camera (CVPR 2010)

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Introduction inspired by recent object recognition work that divides objects into parts – Object class recognition by unsupervised scale-invariant learning [CVPR 2003] – The layout consistent random field for recognizing and segmenting partially occluded objects [CVPR 2006] Two key design goals – Computational efficiency – robustness

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Introduction Depth Image dense probabilistic body part labeling + spatially localized near skeletal joints 3D proposal segmentgenerate

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Introduction We treat the segmentation into body parts as a per-pixel classification task – Evaluating each pixel separately Training data – generate realistic synthetic depth images – train a deep randomized decision forest classifier avoid overfitting

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Introduction Overfitting Simple, discriminative depth comparison image features maintaining high computational efficiency

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Introduction For further speed, the classifier can be run in parallel on each pixel on a GPU mean shift resulting in the 3D joint proposals

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What is Mean Shift ? Non-parametric Density Estimation Non-parametric Density GRADIENT Estimation (Mean Shift) Data Discrete PDF Representation PDF Analysis PDF in feature space Color space Scale space Actually any feature space you can conceive … A tool for: Finding modes in a set of data samples, manifesting an underlying probability density function (PDF) in R N

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region

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Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Objective : Find the densest region

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Treat pose estimation as object recognition – using a novel intermediate body parts representation – spatially localize joints – low computational cost and high accuracy Main contribution

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(i) synthetic depth training data is an excellent proxy for real data (ii) scaling up the learning problem with varied synthetic data is important for high accuracy (iii) our parts-based approach generalizes better than even an oracular exact nearest neighbor Experiments

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Data Depth imaging and Motion capture data Pose estimation research – often focused on techniques – lack of training data Two problems on depth image – color – pose

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Use real mocap data – Retargetted to a variety of base character models – to synthesize a large, varied dataset – 640x480 image at 30 frames per second Depth cameras > Traditional intensity sensors – working in low light levels – giving a calibrated scale estimate – resolving silhouette ambiguities in pose Depth image

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capture a large database of motion capture (mocap) of human actions – approximately 500k frames – (driving, dancing, kicking, running, navigating menus) Need not record mocap with variation in rotation – vertical axis, mirroring left-right, scene position body shape and size, camera pose – all of which can be addedin (semi-)automatically Motion capture data

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The classifier uses no temporal information – static poses – not motion frame to the next are so small as to be insignificant – using ‘furthest neighbor’ clustering algorithm – where the distance between poses – j mean body joints, Pi mean i pose – Define distance more than 5 cm Motion capture data

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necessary to iterate the process of motion capture – sampling from our model – training the classifier – testing joint prediction accuracy CMU mocap database Motion capture data

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build a randomized rendering pipeline – sample fully labeled training images Goals – realism and variety Generating synthetic data

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First : randomly samples a set of parameters Then uses standard computer graphics techniques – render depth and body part images – from texture mapped 3D meshes Use autodesk motionbulider – slight random variation in height – and weight give extra coverage of body shapes – Others parameters

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Generating synthetic data

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Body Part Inference and Joint Proposals Body part labeling Depth image features Randomized decision forests Joint position proposals

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Body part labeling intermediate body part representation – as color-coded – Some directly localize particular skeletal joints – others fill the gaps transforms the problem into one that can readily be solved by efficient classification algorithms

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Body part labeling The parts are specified in a texture map

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Body part labeling 31 body parts: – LU/RU/LW/RW head, neck, – L/R shoulder, LU/RU/LW/RW arm, L/R elbow, L/R wrist, L/R – hand, LU/RU/LW/RW torso, LU/RU/LW/RW leg, L/R knee, – L/R ankle, L/R foot (Left, Right, Upper, loWer)

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Depth image features di (x) is the depth at pixel x in image I Ө= (u, v) describe offsets u and v 1/di (x) ensures the features are depth invariant

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Depth image features Individually these features provide only a weak signal combination in a decision forest – sufficient to accurately – disambiguate all trained parts

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Depth image features The design of these features was strongly motivated by their computational efficiency – no preprocessing is needed – read at most 3 image pixels – at most 5 arithmetic operations – straightforwardly implemented on the GPU

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Randomized decision forests – fast and effective multi-class classifiers – Implemented efficiently on the GPU – 1

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Randomized decision forests

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Joint position proposals generate reliable proposals for the positions of 3D skeletal joints – the final output of our algorithm – used by a tracking algorithm to self initialize – and recover from failure

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Joint position proposals A local mode-finding approach based on mean shift with a weighted Gaussian kernel – ^x i is the reprojection of image pixel xi – bc is a learned per-part bandwidth – world space given depth dI (xi)

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Non-Parametric Density Estimation Assumption : The data points are sampled from an underlying PDF Assumed Underlying PDFReal Data Samples Data point density implies PDF value !

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Assumed Underlying PDFReal Data Samples Non-Parametric Density Estimation

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Assumed Underlying PDFReal Data Samples ? Non-Parametric Density Estimation

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Parametric Density Estimation Assumption : The data points are sampled from an underlying PDF Assumed Underlying PDF Estimate Real Data Samples

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Joint position proposals Wic considers both the inferred body part probability at the pixel and the world surface area of the pixel

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Joint position proposals The detected modes – lie on the surface of the body – pushed back into the scene by a learned z offset produce a final joint position proposal Bandwidth Bc = 0.065m Threshold λc = 0.14 Z offset = 0.039m Set = 5000 images by grid search

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Joint position proposals

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Experiments provide further results in the supplementary material – 3 trees, 20 deep, 300k training images per tree – 2000 training example pixels per image – 2000 candidate features Ө – 50 candidate thresholds ζ per feature

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Experiments Test data – challenging synthetic and real depth images to evaluate our approach – synthesize 5000 depth images Real test set – 8808 frames of real depth images – 15 different subjects – 7 upper body joint positions

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Experiments Error metric: – quantify both classification average of the diagonal of the confusion matrix between the ground truth part label and the most likely inferred part label – Joint prediction accuracy generate recall-precision curvesas a function of confidence threshold quantify accuracy as average precision per joint

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Experiments Error metric: – This penalizes multiple spurious detections – Near the correct position which might slow a downstream tracking algorithm D = 0.1 m below closed real test data

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Experiments

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Real time motion capture using a single time-of-flight camera. [CVPR 2010]

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Discussion accurate proposals – for the 3D locations of body joints – super real-time from single depth images body part recognition – as an intermediate representation a highly varied synthetic training set – train very deep decision forests – Depth invariant features without overfitting

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Future work study of the variability in the source mocap data Generative model underlying the synthesis pipeline a similarly efficient approach – directly regress joint positions – remove ambiguities in local pose

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Thank you

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