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Articulated Pose Estimation with Flexible Mixtures of Parts

Articulated Pose Estimation with Flexible Mixtures of Parts

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Articulated Pose Estimation with Flexible Mixtures of Parts

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  1. Articulated Pose Estimation with Flexible Mixtures of Parts Yi Yang & Deva Ramanan University of California, Irvine

  2. Goal Articulated pose estimation ( ) recovers the pose of an articulated object which consists of joints and rigid parts

  3. Applications

  4. Unconstrained Images

  5. Classic Approach Part Representation • Head, Torso, Arm, Leg • Location, Rotation, Scale Marr & Nishihara 1978

  6. Classic Approach Part Representation • Head, Torso, Arm, Leg • Location, Rotation, Scale Marr & Nishihara 1978 Pictorial Structure • Unary Templates • Pairwise Springs Fischler & Elschlager 1973 Felzenszwalb & Huttenlocher 2005

  7. Classic Approach Part Representation • Head, Torso, Arm, Leg • Location, Rotation, Scale Marr & Nishihara 1978 Pictorial Structure • Unary Templates • Pairwise Springs Fischler & Elschlager 1973 Felzenszwalb & Huttenlocher 2005 Lan & Huttenlocher 2005 Andriluka etc. 2009 Sigal & Black 2006 Eichner etc. 2009 Singh etc. 2010 Ramanan 2007 Epshteian & Ullman 2007 Johnson & Everingham 2010 Wang & Mori 2008 Sapp etc. 2010 Ferrari etc. 2008 Tran & Forsyth 2010

  8. Problem: Wide Variations

  9. Problem: Wide Variations

  10. Problem: Wide Variations Naïve brute-force evaluation is expensive

  11. Our Method – “Mini-Parts” Key idea: “mini part” model can approximate deformations

  12. Example: Arm Approximation

  13. Example: Torso Approximation

  14. Key Advantages • Flexibility: General affine warps (orientation, foreshortening, …) • Speed: Mixtures of local templates + dynamic programming

  15. Pictorial Structure Model • : Image • : Location of part

  16. Pictorial Structure Model • : Unary template for part • : Local image features at location

  17. Pictorial Structure Model • : Spatial features between and • : Pairwise springs between part and part

  18. Our Flexible Mixture Model • : Mixture of part

  19. Our Flexible Mixture Model • : Mixture of part • : Unary template for part with mixture

  20. Our Flexible Mixture Model • : Mixture of part • : Unary template for part with mixture • : Pairwise springs between part with mixture and part with mixture

  21. Our Flexible Mixture Model • : Mixture of part • : Unary template for part with mixture • : Pairwise springs between part with mixture and part with mixture

  22. Co-occurrence “Bias” • : Pairwise co-occurrence prior between part with mixture and part with mixture

  23. Co-occurrence “Bias” • : Pairwise co-occurrence prior between part with mixture and part with mixture

  24. Co-occurrence “Bias” • : Pairwise co-occurrence prior between part with mixture and part with mixture

  25. Inference & Learning Inference For a tree graph (V,E): dynamicprogramming

  26. Inference & Learning Inference For a tree graph (V,E): dynamicprogramming Given labeled positive and negative , write , and Learning

  27. Benchmark Datasets

  28. How to Get Part Mixtures? Solution: Cluster relative locations of joints w.r.t. parents

  29. Articulation

  30. Articulation parts, mixtures unique pictorial structures

  31. Articulation parts, mixtures unique pictorial structures Not all are equally likely --- “prior” given by

  32. Qualitative Results

  33. Quantitative Results on PARSE % of correctly localized limbs All previous work use explicitly articulated models

  34. Quantitative Results on PARSE % of correctly localized limbs 1 second per image

  35. More Parts and Mixtures Help 14 parts (joints) 27 parts (joints + midpoints)

  36. Quantitative Results on BUFFY % of correctly localized limbs Our algorithm = 5 seconds -vs- Next best = 5 minutes

  37. Quantitative Results on BUFFY % of correctly localized limbs All previous work use explicitly articulated models

  38. Human Detection

  39. Conclusion • Model affine warps with a part-based model

  40. Conclusion • Model affine warps with a part-based model • Exponential set of pictorial structures

  41. Conclusion • Model affine warps with a part-based model • Exponential set of pictorial structures • Flexible vs rigid relations

  42. Conclusion • Model affine warps with a part-based model • Exponential set of pictorial structures • Flexible vs rigid relations • Supervision helps

  43. Thank you