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1506.02178

Yana edited this page Apr 11, 2018 · 3 revisions

IJCV 2016

[arxiv 1506.02178] Capturing Hands in Action using Discriminative Salient Points and Physics Simulation [PDF] [project page] [notes]

Dimitrios Tzionas, Luca Ballan, Abhilash Srikantha, Pablo Aponte, Marc Pollefeys, Juergen Gall

Objective

Track hands that interact with each other and objects (rigid & articulated)

single objective function that aligns the model with the observed data, with collision and physical model that tests for stability

Synthesis

generative model + discriminative classifier trained on salient points (fingertips)

physics simulation: test configuration by taking into account friction, gravity, mass and verify object displacement as proxy for stability

collision detection : avoid inter-penetration

Objective function almost everywhere differentiable ==> can be optimized with standard optimization techniques

37 DoF for the hand

Pre-processsing

  • Segmentation of hand + object by removing irrelevant depths
  • skin color segmentation to separate hand and object

Objective function

Takes into account :

  • coherence between mesh and depth data

    • depth from model, and 3d Chamer distance (point to point or point to plane using normal info)
    • from data to model only with depth discontinuities (otherwise expensive to evaluate because recomputed for each new model)
  • consistency of the estimates of the hands and the manipulated object

    • fingertip detector
    • manual fingertip annotation
    • Hough forest on patches
    • high threshold to only recover high confidence fingertip detections
  • penalization of collisions (intersections)

    • bounding volume hierarchy (BVH) to detect colisions BVH: tree-like structure that encloses objects in larger volumes that contain an increasing number of objects, if parent volumes do not intersect, children volumes do not either so speed of collision detection is increased.
  • physics

    • penalize distance of object to identified contact point
  • anatomical joint limits

    • exponential around stability postion
    • (Albrecht and al 2003)
  • regularization by penalizing difference from previously estimated joint angles

Physics simulation

  • test first if estimate is 'physically correct' : if center did not move significantly (less then 3mm in 35 iterations)
  • if not, find distance between finger parts (phallanges) and the object. If distance <10mm : finger part is candidate for contact
  • combination between 2 and 4 candidates are tested
  • select combination with lowest displacement of object
  • introduce an energy that forces contact for those points

Results

Evaluation on Dexter dataset

error metric for our experiments is the 2D dis- tance (pixels) between the projection of the 3D joints and the corresponding 2D annotations

on 14 sequences without objects : 11 for training, 3 for testing

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