Slide 1

Cascaded Models for Articulated Pose Estimation Ben Sapp, Alexander Toshev and Ben Taskar University of Pennsylvania

Slide 2

Human Pose Estimation Goal: Image -> Stick Figure 2D locations of anatomical parts from a single image efficient inference input output

Slide 3

Human Pose Estimation: Its Hard lighting variation background clutter pose variation intrinsic scale variations foreshortening

Slide 4

Articulated Pose and Pictorial Structures A popular choice for (articulated) parts-based models A non-exhaustive timeline Fischler & Elschlager The representation and matching of pictorial structures Felzenszwalb & Huttenlocher PS for Object Recognition Ramanan Learning to Parse Images of Articulated Objects Felzenszwalb et al. A Discriminatively Trained, Multiscale, Deformable Part Model. Ferrari et al. Progressive Search Space Reduction 1972200520062008 2009 2010 Eichner & Ferrari Better Appearance Models for Pictorial Structures Andriluka et al. Pictorial Structures Re-visited Fergus et al. ICCV Short Course Sapp et al. Adaptive Pose Priors for PS

Slide 5

Background: How PS works x y head part detectors unary score: detection maps pairwise score: geometric prior llarm rlarm ruarm luarm torso : location for part i max-product inference sum-product inference prediction

Slide 6

Background: The Complexity of PS state space for part i typical state space size: n > 150,000 states x y head llarm rlarm ruarm luarm torso Standard inference in a tree graphical model is Typical # of valid combinations for two neighboring parts: (80 x 80 x 24) (80/5 x 80/5 x 24) 1 billion state-pairs! x = pairwise computation:

Slide 7

Background: The Complexity of PS If, efficient inference tricks can be used: [Felzenszwalb & Huttenlocher, 2005 ] Max-prod w/ unimodal cost: Distance transform for Sum-prod w/ linear filter cost: Convolution for Q: Are we losing too much in expressivity for this gain in efficiency? + score for part-state pair: + unary i unary j pairwise i,j State-of-the-art:

Slide 8

Goal: Integrating richer pairwise terms (, ) e.g., distance in color distribution: incorporate image evidence

Slide 9

Computation example 20x20 grid 24 angles cpu time