yi yang & deva ramanan university of california, irvine
TRANSCRIPT
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- Yi Yang & Deva Ramanan University of California, Irvine
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- Goal Articulated pose estimation ( ) recovers the pose of an articulated object which consists of joints and rigid parts
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- Applications ActionHCIGaming SegmentationObject Detection
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- Unconstrained Images
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- Classic Approach Part Representation Head, Torso, Arm, Leg Location, Rotation, Scale Marr & Nishihara 1978
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- Classic Approach Part Representation Head, Torso, Arm, Leg Location, Rotation, Scale Fischler & Elschlager 1973 Felzenszwalb & Huttenlocher 2005 Marr & Nishihara 1978 Pictorial Structure Unary Templates Pairwise Springs
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- Classic Approach Part Representation Head, Torso, Arm, Leg Location, Rotation, Scale Fischler & Elschlager 1973 Felzenszwalb & Huttenlocher 2005 Marr & Nishihara 1978 Andriluka etc. 2009 Eichner etc. 2009 Johnson & Everingham 2010 Sapp etc. 2010 Singh etc. 2010 Tran & Forsyth 2010 Epshteian & Ullman 2007 Ferrari etc. 2008 Lan & Huttenlocher 2005 Ramanan 2007 Sigal & Black 2006 Wang & Mori 2008 Pictorial Structure Unary Templates Pairwise Springs
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- Problem: Wide Variations In-plane rotationForeshortening
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- Problem: Wide Variations In-plane rotationForeshortening ScalingOut-of-plane rotation Intra-category variationAspect ratio
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- Problem: Wide Variations In-plane rotationForeshortening ScalingOut-of-plane rotation Intra-category variationAspect ratio Nave brute-force evaluation is expensive
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- Our Method Mini-Parts Key idea: mini part model can approximate deformations
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- Example: Arm Approximation
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- Example: Torso Approximation
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- Key Advantages Flexibility: General affine warps (orientation, foreshortening, ) Speed: Mixtures of local templates + dynamic programming
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- Pictorial Structure Model
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- Our Flexible Mixture Model
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- Co-occurrence Bias
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- Inference & Learning Inference
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- Inference & Learning Learning Inference
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- Benchmark Datasets PARSE Full-body http://www.ics.uci.edu/~drama nan/papers/parse/index.html BUFFY Upper-body http://www.robots.ox.ac.uk/~v gg/data/stickmen/index.html
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- Solution: Cluster relative locations of joints w.r.t. parents How to Get Part Mixtures?
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- Articulation
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- Qualitative Results
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- Quantitative Results on PARSE All previous work use explicitly articulated models Image Parse Testset MethodTotal Ramanan 200727.2 Andrikluka 200955.2 Johnson 200956.4 Singh 201060.9 Johnson 201066.2 Our Model74.9 % of correctly localized limbs
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- Quantitative Results on PARSE 1 second per image Image Parse Testset MethodHeadTorsoU. LegsL. LegsU. ArmsL. ArmsTotal Ramanan 200752.137.531.029.017.513.627.2 Andrikluka 200981.475.663.255.147.631.755.2 Johnson 200977.668.861.554.953.239.356.4 Singh 201091.276.671.564.950.034.260.9 Johnson 201085.476.173.465.464.746.966.2 Our Model97.693.283.975.172.048.374.9 % of correctly localized limbs
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- More Parts and Mixtures Help 14 parts (joints)27 parts (joints + midpoints)
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- Quantitative Results on BUFFY Our algorithm = 5 seconds -vs- Next best = 5 minutes Subset of Buffy Testset MethodTotal Tran 201062.3 Andrikluka 200973.5 Eichner 200980.1 Sapp 2010a85.9 Sapp 2010b85.5 Our Model89.1 % of correctly localized limbs
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- Quantitative Results on BUFFY All previous work use explicitly articulated models Subset of Buffy Testset MethodHeadTorsoU. ArmsL. ArmsTotal Tran 2010 --- 62.3 Andrikluka 200990.795.579.341.273.5 Eichner 200998.797.982.859.880.1 Sapp 2010a100 91.165.785.9 Sapp 2010b10096.295.363.085.5 Our Model10099.696.670.989.1 % of correctly localized limbs
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- Human Detection
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- Conclusion Model affine warps with a part-based model
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- Conclusion Model affine warps with a part-based model Exponential set of pictorial structures
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- Conclusion Model affine warps with a part-based model Exponential set of pictorial structures Flexible vs rigid relations
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- Conclusion Model affine warps with a part-based model Exponential set of pictorial structures Flexible vs rigid relations Supervision helps
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- Thank you