Picture code illustration for 3D face analysis

gistfile1.jl

# https://mrkulk.github.io/www_cvpr15/1999.pdf

function PROGRAM(MU, PC, EV, VERTEX_ORDER)
  # Scene Language: Stochastic Scene Gen
  face=Dict();
  shape = [];
  texture = [];
  for S in ["shape", "texture"]
    for p in ["nose", "eyes", "outline", "lips"]
      coeff = MvNormal(0,1,1,99)
      face[S][p] = MU[S][p]+PC[S][p].*(coeff.*EV[S][p])
    end
  end
  shape=face["shape"][:];
  tex=face["texture"][:];
  camera = Uniform(-1,1,1,2);
  light = Uniform(-1,1,1,2)

  # Approximate Renderer
  rendered_img = MeshRenderer(shape,tex,light,camera)

  # Representation Layer
  ren_ftrs = getFeatures("CNN_Conv6", rendered_img)

  # Comparator
  # Using Pixel as Summary Statistics
  observe(MvNormal(0,0.01), rendered_img-obs_img)

  #Using CNN last conv layer as Summary Statistics
  observe(MvNormal(0,10), ren_ftrs-obs_cnn)
end

global obs_img = imread("test.png")
global obs_cnn = getFeatures("CNN_Conv6", img)

#Load args from file
TR = trace(PROGRAM,args=[MU,PC,EV,VERTEX_ORDER])

# Data-Driven Learning
learn_datadriven_proposals(TR,100000,"CNN_Conv6")
load_proposals(TR)

# Inference
infer(TR,CB,20,["DATA-DRIVEN"])
infer(TR,CB,200,["ELLIPTICAL"])