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Implicit Probabilistic Models of Human Motion for Synthesis and Tracking Hedvig Sidenbladh, KTH, Sweden (now FOI, Sweden) Michael J. Black, Brown University, USA Leonid Sigal, Brown University, USA
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Articulated 3D tracking = model parameters I = image Recursive Bayesian formulation:
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Extreme case Non-linear motion, strong dependencies Model dependencies analytically –e.g. [Hogg, Rohr] for walking Dynamical models –e.g. [Wren&Pentland, Bruderlin&Calvert] Learn from Mocap examples –e.g. [Bowden, Brand, Molina&Hilton] Modeling Human Motion
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Texture Synthesis Efros & Freeman’01 “Database” Synthetic Texture –e.g. [De Bonnet&Viola, Efros&Leung, Efros&Freeman, Paztor&Freeman, Hertzmann et al] –Image(s) as an implicit probabilistic model.
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Motion Texture –Motion examples as an implicit probabilistic model.
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Probabilistic Formulation Generative model: Problem: Model for all motions i in the database! No learning required, all the variability in the data captured. Database example i Sampling from, taking t = i
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Probabilistic Database Search Sort database in some way to enable search in sublinear time Linear search infeasible for large database! No need to visit all database examples - only need to sample from distribution time joint angles c =[c 1, c 2, c 3, c 4 ] Sort into tree-structure according to PCA coefficients
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Probabilistic Database Search Each level in the tree corresponds to one coefficient l. Sort examples i into tree: Left subtree for negative value of c l,i, right for positive value.
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Probabilistic Database Search Approximated by sampling from tree iteratively:
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Synthesis Running Walking Small database with running, walking, skipping, dance etc. Changing color indicates new example sequence. Future work: Add editing possibility, gravity model, goal function. Goal: Generate smooth and plausible-looking motion.
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Tracking Goal: Efficiently generate samples (image data will sort out which are good). Temperature parameter controls randomness of tree search.
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Arm Tracking Example Constant velocity model 1000 samples ~1 min/frame Image likelihood model from [Sidenbladh & Black, ICCV 01] Example based model
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”Mocap Soup” [Cohen] at SIGGRAPH 02 –Arikan & Forsyth. Interactive motion generation from examples –Li et al. Motion textures: A two-level statistical model for character motion synthesis –Lee et al. Interactive control of avatars animated with human motion data –Kovar et al. Motion graphs –Pullen & Bregler. Motion capture assisted animation: Texturing and synthesis Here we formulate a probabilistic model suitable for stochastic search search and Bayesian tracking.
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Conclusions Implicit motion model - replace learning with search Analog to example based texture synthesis Larger database - sub-linear time search Tree structure sorted with PCA coefficients Probabilistic tree search - sampling from the tree approximates sampling from
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